diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml
index 8a0f812..9651a54 100644
--- a/.github/workflows/run-tests.yml
+++ b/.github/workflows/run-tests.yml
@@ -46,13 +46,12 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install tox coverage
+          pip install "tox<4.24" coverage
 
       - name: Run tests
         run: >-
-          pipx run --python '${{ steps.setup-python.outputs.python-path }}'
           tox
-          -- -rFEx --durations 10 --color yes --cov --cov-branch --cov-report=xml  # pytest args
+          -- -rFEx --durations 10 --color yes --cov --cov-branch --cov-report=xml
 
       - name: Check for codecov token availability
         id: codecov-check
diff --git a/docs/tutorials/1-attribution-motif-discovery.ipynb b/docs/tutorials/1-attribution-motif-discovery.ipynb
index 7be8cb6..bfbe3e2 100644
--- a/docs/tutorials/1-attribution-motif-discovery.ipynb
+++ b/docs/tutorials/1-attribution-motif-discovery.ipynb
@@ -18,7 +18,414 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
+    "## Selecting genes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can view all the genes in Decima's dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chrom</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>strand</th>\n",
+       "      <th>gene_type</th>\n",
+       "      <th>frac_nan</th>\n",
+       "      <th>mean_counts</th>\n",
+       "      <th>n_tracks</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>gene_length</th>\n",
+       "      <th>gene_mask_start</th>\n",
+       "      <th>gene_mask_end</th>\n",
+       "      <th>frac_N</th>\n",
+       "      <th>fold</th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>pearson</th>\n",
+       "      <th>size_factor_pearson</th>\n",
+       "      <th>ensembl_canonical_tss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>STRADA</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>63381538</td>\n",
+       "      <td>63905826</td>\n",
+       "      <td>-</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.208074</td>\n",
+       "      <td>7616</td>\n",
+       "      <td>63682336</td>\n",
+       "      <td>63741986</td>\n",
+       "      <td>59650</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>223490</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold1']</td>\n",
+       "      <td>train</td>\n",
+       "      <td>ENSG00000266173</td>\n",
+       "      <td>0.469923</td>\n",
+       "      <td>0.476627</td>\n",
+       "      <td>63741799.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ETV4</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>43219172</td>\n",
+       "      <td>43743460</td>\n",
+       "      <td>-</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.030873</td>\n",
+       "      <td>0.925863</td>\n",
+       "      <td>5004</td>\n",
+       "      <td>43527844</td>\n",
+       "      <td>43579620</td>\n",
+       "      <td>51776</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>215616</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold1']</td>\n",
+       "      <td>train</td>\n",
+       "      <td>ENSG00000175832</td>\n",
+       "      <td>0.738092</td>\n",
+       "      <td>0.613281</td>\n",
+       "      <td>43546340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>USP25</th>\n",
+       "      <td>chr21</td>\n",
+       "      <td>15566185</td>\n",
+       "      <td>16090473</td>\n",
+       "      <td>+</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.650355</td>\n",
+       "      <td>8604</td>\n",
+       "      <td>15730025</td>\n",
+       "      <td>15880069</td>\n",
+       "      <td>150044</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>313884</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold6']</td>\n",
+       "      <td>train</td>\n",
+       "      <td>ENSG00000155313</td>\n",
+       "      <td>0.905222</td>\n",
+       "      <td>0.784446</td>\n",
+       "      <td>15729982.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZSWIM5</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>44945761</td>\n",
+       "      <td>45470049</td>\n",
+       "      <td>-</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.000620</td>\n",
+       "      <td>2.190115</td>\n",
+       "      <td>6123</td>\n",
+       "      <td>45016399</td>\n",
+       "      <td>45306209</td>\n",
+       "      <td>289810</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>453650</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold5']</td>\n",
+       "      <td>train</td>\n",
+       "      <td>ENSG00000162415</td>\n",
+       "      <td>0.961772</td>\n",
+       "      <td>0.795131</td>\n",
+       "      <td>45206605.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C21orf58</th>\n",
+       "      <td>chr21</td>\n",
+       "      <td>45963427</td>\n",
+       "      <td>46487715</td>\n",
+       "      <td>-</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.000791</td>\n",
+       "      <td>1.650467</td>\n",
+       "      <td>7354</td>\n",
+       "      <td>46300181</td>\n",
+       "      <td>46323875</td>\n",
+       "      <td>23694</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>187534</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold6']</td>\n",
+       "      <td>train</td>\n",
+       "      <td>ENSG00000160298</td>\n",
+       "      <td>0.645268</td>\n",
+       "      <td>0.412368</td>\n",
+       "      <td>46323870.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NPDC1</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>136685731</td>\n",
+       "      <td>137210019</td>\n",
+       "      <td>-</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.625285</td>\n",
+       "      <td>7852</td>\n",
+       "      <td>137039463</td>\n",
+       "      <td>137046179</td>\n",
+       "      <td>6716</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>170556</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold3']</td>\n",
+       "      <td>test</td>\n",
+       "      <td>ENSG00000107281</td>\n",
+       "      <td>0.316322</td>\n",
+       "      <td>0.178204</td>\n",
+       "      <td>137046177.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZNF425</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>148765876</td>\n",
+       "      <td>149290164</td>\n",
+       "      <td>-</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.001048</td>\n",
+       "      <td>1.292957</td>\n",
+       "      <td>6511</td>\n",
+       "      <td>149102784</td>\n",
+       "      <td>149126324</td>\n",
+       "      <td>23540</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>187380</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold7']</td>\n",
+       "      <td>train</td>\n",
+       "      <td>ENSG00000204947</td>\n",
+       "      <td>0.821292</td>\n",
+       "      <td>0.737081</td>\n",
+       "      <td>149126324.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COL5A1</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>134477934</td>\n",
+       "      <td>135002222</td>\n",
+       "      <td>+</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.002159</td>\n",
+       "      <td>1.492664</td>\n",
+       "      <td>6209</td>\n",
+       "      <td>134641774</td>\n",
+       "      <td>134844843</td>\n",
+       "      <td>203069</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>366909</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold3']</td>\n",
+       "      <td>test</td>\n",
+       "      <td>ENSG00000130635</td>\n",
+       "      <td>0.766624</td>\n",
+       "      <td>0.456999</td>\n",
+       "      <td>134641803.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRD3</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>133708087</td>\n",
+       "      <td>134232375</td>\n",
+       "      <td>-</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.190450</td>\n",
+       "      <td>8675</td>\n",
+       "      <td>134030305</td>\n",
+       "      <td>134068535</td>\n",
+       "      <td>38230</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>202070</td>\n",
+       "      <td>0.004662</td>\n",
+       "      <td>['fold3']</td>\n",
+       "      <td>test</td>\n",
+       "      <td>ENSG00000169925</td>\n",
+       "      <td>0.344062</td>\n",
+       "      <td>0.280283</td>\n",
+       "      <td>134068026.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EVI5L</th>\n",
+       "      <td>chr19</td>\n",
+       "      <td>7666393</td>\n",
+       "      <td>8190681</td>\n",
+       "      <td>+</td>\n",
+       "      <td>protein_coding</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.959605</td>\n",
+       "      <td>7570</td>\n",
+       "      <td>7830233</td>\n",
+       "      <td>7864976</td>\n",
+       "      <td>34743</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>198583</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>['fold3']</td>\n",
+       "      <td>test</td>\n",
+       "      <td>ENSG00000142459</td>\n",
+       "      <td>0.810152</td>\n",
+       "      <td>0.704828</td>\n",
+       "      <td>7830218.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>18457 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chrom      start        end strand       gene_type  frac_nan  \\\n",
+       "STRADA    chr17   63381538   63905826      -  protein_coding  0.000000   \n",
+       "ETV4      chr17   43219172   43743460      -  protein_coding  0.030873   \n",
+       "USP25     chr21   15566185   16090473      +  protein_coding  0.000000   \n",
+       "ZSWIM5     chr1   44945761   45470049      -  protein_coding  0.000620   \n",
+       "C21orf58  chr21   45963427   46487715      -  protein_coding  0.000791   \n",
+       "...         ...        ...        ...    ...             ...       ...   \n",
+       "NPDC1      chr9  136685731  137210019      -  protein_coding  0.000000   \n",
+       "ZNF425     chr7  148765876  149290164      -  protein_coding  0.001048   \n",
+       "COL5A1     chr9  134477934  135002222      +  protein_coding  0.002159   \n",
+       "BRD3       chr9  133708087  134232375      -  protein_coding  0.000000   \n",
+       "EVI5L     chr19    7666393    8190681      +  protein_coding  0.000000   \n",
+       "\n",
+       "          mean_counts  n_tracks  gene_start   gene_end  gene_length  \\\n",
+       "STRADA       2.208074      7616    63682336   63741986        59650   \n",
+       "ETV4         0.925863      5004    43527844   43579620        51776   \n",
+       "USP25        3.650355      8604    15730025   15880069       150044   \n",
+       "ZSWIM5       2.190115      6123    45016399   45306209       289810   \n",
+       "C21orf58     1.650467      7354    46300181   46323875        23694   \n",
+       "...               ...       ...         ...        ...          ...   \n",
+       "NPDC1        2.625285      7852   137039463  137046179         6716   \n",
+       "ZNF425       1.292957      6511   149102784  149126324        23540   \n",
+       "COL5A1       1.492664      6209   134641774  134844843       203069   \n",
+       "BRD3         3.190450      8675   134030305  134068535        38230   \n",
+       "EVI5L        1.959605      7570     7830233    7864976        34743   \n",
+       "\n",
+       "          gene_mask_start  gene_mask_end    frac_N       fold dataset  \\\n",
+       "STRADA             163840         223490  0.000000  ['fold1']   train   \n",
+       "ETV4               163840         215616  0.000000  ['fold1']   train   \n",
+       "USP25              163840         313884  0.000000  ['fold6']   train   \n",
+       "ZSWIM5             163840         453650  0.000000  ['fold5']   train   \n",
+       "C21orf58           163840         187534  0.000000  ['fold6']   train   \n",
+       "...                   ...            ...       ...        ...     ...   \n",
+       "NPDC1              163840         170556  0.000000  ['fold3']    test   \n",
+       "ZNF425             163840         187380  0.000000  ['fold7']   train   \n",
+       "COL5A1             163840         366909  0.000000  ['fold3']    test   \n",
+       "BRD3               163840         202070  0.004662  ['fold3']    test   \n",
+       "EVI5L              163840         198583  0.000000  ['fold3']    test   \n",
+       "\n",
+       "                  gene_id   pearson  size_factor_pearson  \\\n",
+       "STRADA    ENSG00000266173  0.469923             0.476627   \n",
+       "ETV4      ENSG00000175832  0.738092             0.613281   \n",
+       "USP25     ENSG00000155313  0.905222             0.784446   \n",
+       "ZSWIM5    ENSG00000162415  0.961772             0.795131   \n",
+       "C21orf58  ENSG00000160298  0.645268             0.412368   \n",
+       "...                   ...       ...                  ...   \n",
+       "NPDC1     ENSG00000107281  0.316322             0.178204   \n",
+       "ZNF425    ENSG00000204947  0.821292             0.737081   \n",
+       "COL5A1    ENSG00000130635  0.766624             0.456999   \n",
+       "BRD3      ENSG00000169925  0.344062             0.280283   \n",
+       "EVI5L     ENSG00000142459  0.810152             0.704828   \n",
+       "\n",
+       "          ensembl_canonical_tss  \n",
+       "STRADA               63741799.0  \n",
+       "ETV4                 43546340.0  \n",
+       "USP25                15729982.0  \n",
+       "ZSWIM5               45206605.0  \n",
+       "C21orf58             46323870.0  \n",
+       "...                         ...  \n",
+       "NPDC1               137046177.0  \n",
+       "ZNF425              149126324.0  \n",
+       "COL5A1              134641803.0  \n",
+       "BRD3                134068026.0  \n",
+       "EVI5L                 7830218.0  \n",
+       "\n",
+       "[18457 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from decima import DecimaResult\n",
+    "result = DecimaResult.load()\n",
+    "result.gene_metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will start by analyzing the SPI1 and BRD3 genes. SPI1 is a key transcription factor in myeloid cell development. We'll examine its regulation across different monocyte and macrophage cell types where it is known to be important."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## CLI API"
    ]
   },
@@ -26,103 +433,86 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at a simple example using Decima's CLI API to analyze the SPI1 and BRD3 genes. SPI1 is a key transcription factor in myeloid cell development. We'll examine its regulation across different monocyte and macrophage cell types where it is known to be important."
+    "Let's look at a simple example using Decima's CLI API to analyze the SPI1 and BRD3 genes."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:24:58.683436Z",
-     "iopub.status.busy": "2025-11-21T06:24:58.683299Z",
-     "iopub.status.idle": "2025-11-21T06:25:56.147216Z",
-     "shell.execute_reply": "2025-11-21T06:25:56.146610Z"
-    }
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Usage: decima attributions [OPTIONS]\r\n",
-      "\r\n",
-      "  Generate and save attribution analysis results for a gene or a set of\r\n",
-      "  sequences and perform seqlet calling on the attributions.\r\n",
-      "\r\n",
-      "  Output files:\r\n",
-      "\r\n",
-      "      ├── {output_prefix}.attributions.h5      # Raw attribution score matrix\r\n",
-      "      per gene.\r\n",
-      "\r\n",
-      "      ├── {output_prefix}.attributions.bigwig  # Genome browser track of\r\n",
-      "      attribution as bigwig file.\r\n",
-      "\r\n",
-      "      ├── {output_prefix}.seqlets.bed          # List of attribution peaks in\r\n",
-      "      BED format.\r\n",
-      "\r\n",
-      "      ├── {output_prefix}.motifs.tsv           # Detected motifs in peak\r\n",
-      "      regions.\r\n",
-      "\r\n",
-      "      └── {output_prefix}.warnings.qc.log      # QC warnings about prediction\r\n",
-      "      reliability.\r\n",
-      "\r\n",
-      "  Examples:\r\n",
-      "\r\n",
-      "      >>> decima attributions -o output_prefix -g SPI1\r\n",
-      "\r\n",
-      "      >>> decima attributions -o output_prefix -g SPI1,CD68 --tasks \"cell_type\r\n",
-      "      == 'classical monocyte'\" --device 0\r\n",
-      "\r\n",
-      "      >>> decima attributions -o output_prefix --seqs tests/data/seqs.fasta\r\n",
-      "      --tasks \"cell_type == 'classical monocyte'\" --device 0\r\n",
-      "\r\n",
-      "Options:\r\n",
-      "  -o, --output-prefix TEXT        Prefix path to the output files  [required]\r\n",
-      "  -g, --genes TEXT                Comma-separated list of gene symbols or IDs\r\n",
-      "                                  to analyze.\r\n",
-      "  --seqs TEXT                     Path to a file containing sequences to\r\n",
-      "                                  analyze\r\n",
-      "  --tasks TEXT                    Query string to filter cell types to analyze\r\n",
-      "                                  attributions for (e.g. 'cell_type ==\r\n",
-      "                                  'classical monocyte'')\r\n",
-      "  --off-tasks TEXT                Optional query string to filter cell types\r\n",
-      "                                  to contrast against.\r\n",
-      "  --model TEXT                    Model to use for attribution analysis either\r\n",
-      "                                  replicate number or path to the model.\r\n",
-      "                                  [default: ensemble]\r\n",
-      "  --metadata TEXT                 Path to the metadata anndata file or name of\r\n",
-      "                                  the model. If not provided, the compabilite\r\n",
-      "                                  metadata for the model will be used.\r\n",
-      "  --method TEXT                   Method to use for attribution analysis.\r\n",
-      "  --transform [specificity|aggregate]\r\n",
-      "                                  Transform to use for attribution analysis.\r\n",
-      "  --num-workers INTEGER           Number of workers for attribution analysis.\r\n",
-      "  --tss-distance INTEGER          TSS distance for attribution analysis.\r\n",
-      "  --batch-size INTEGER            Batch size for attribution analysis.\r\n",
-      "  --top-n-markers INTEGER         Top n markers to predict. If not provided,\r\n",
-      "                                  all markers will be predicted.\r\n",
-      "  --threshold FLOAT               Threshold for attribution analysis.\r\n",
-      "  --min-seqlet-len INTEGER        Minimum length for seqlet calling.\r\n",
-      "  --max-seqlet-len INTEGER        Maximum length for seqlet calling.\r\n",
-      "  --additional-flanks INTEGER     Additional flanks for seqlet calling.\r\n",
-      "  --pattern-type [both|pos|neg]   Type of pattern to call.\r\n",
-      "  --meme-motif-db TEXT            Path to the MEME motif database.  [default:\r\n",
-      "                                  hocomoco_v13]\r\n",
-      "  --device TEXT                   Device to use for attribution analysis.\r\n",
-      "  --genome TEXT                   Genome name or path to the genome fasta\r\n",
-      "                                  file.  [default: hg38]\r\n",
-      "  --help                          Show this message and exit.\r\n",
+      "Usage: decima attributions [OPTIONS]\n",
+      "\n",
+      "  Generate and save attribution analysis results for a gene or a set of\n",
+      "  sequences and perform seqlet calling on the attributions.\n",
+      "\n",
+      "  Output files:\n",
+      "\n",
+      "      ├── {output_prefix}.attributions.h5      # Raw attribution score matrix\n",
+      "      per gene.\n",
+      "\n",
+      "      ├── {output_prefix}.attributions.bigwig  # Genome browser track of\n",
+      "      attribution as bigwig file.\n",
+      "\n",
+      "      ├── {output_prefix}.seqlets.bed          # List of attribution peaks in\n",
+      "      BED format.\n",
+      "\n",
+      "      ├── {output_prefix}.motifs.tsv           # Detected motifs in peak\n",
+      "      regions.\n",
+      "\n",
+      "      └── {output_prefix}.warnings.qc.log      # QC warnings about prediction\n",
+      "      reliability.\n",
+      "\n",
+      "  Examples:\n",
+      "\n",
+      "      >>> decima attributions -o output_prefix -g SPI1\n",
+      "\n",
+      "      >>> decima attributions -o output_prefix -g SPI1,CD68 --tasks \"cell_type\n",
+      "      == 'classical monocyte'\" --device 0\n",
+      "\n",
+      "      >>> decima attributions -o output_prefix --seqs tests/data/seqs.fasta\n",
+      "      --tasks \"cell_type == 'classical monocyte'\" --device 0\n",
+      "\n",
+      "Options:\n",
+      "  -o, --output-prefix TEXT        Prefix path to the output files  [required]\n",
+      "  -g, --genes TEXT                Comma-separated list of gene symbols or IDs\n",
+      "                                  to analyze.\n",
+      "  --seqs TEXT                     Path to a file containing sequences to\n",
+      "                                  analyze\n",
+      "  --tasks TEXT                    Query string to filter cell types to analyze\n",
+      "                                  attributions for (e.g. 'cell_type ==\n",
+      "                                  'classical monocyte'')\n",
+      "  --off-tasks TEXT                Optional query string to filter cell types\n",
+      "                                  to contrast against.\n",
+      "  --model TEXT                    Model to use for attribution analysis either\n",
+      "                                  replicate number or path to the model.\n",
+      "                                  [default: ensemble]\n",
+      "  --metadata TEXT                 Path to the metadata anndata file or name of\n",
+      "                                  the model. If not provided, the compabilite\n",
+      "                                  metadata for the model will be used.\n",
+      "  --method TEXT                   Method to use for attribution analysis.\n",
+      "  --transform [specificity|aggregate]\n",
+      "                                  Transform to use for attribution analysis.\n",
+      "  --num-workers INTEGER           Number of workers for attribution analysis.\n",
+      "  --tss-distance INTEGER          TSS distance for attribution analysis.\n",
+      "  --batch-size INTEGER            Batch size for attribution analysis.\n",
+      "  --top-n-markers INTEGER         Top n markers to predict. If not provided,\n",
+      "                                  all markers will be predicted.\n",
+      "  --threshold FLOAT               Threshold for attribution analysis.\n",
+      "  --min-seqlet-len INTEGER        Minimum length for seqlet calling.\n",
+      "  --max-seqlet-len INTEGER        Maximum length for seqlet calling.\n",
+      "  --additional-flanks INTEGER     Additional flanks for seqlet calling.\n",
+      "  --pattern-type [both|pos|neg]   Type of pattern to call.\n",
+      "  --meme-motif-db TEXT            Path to the MEME motif database.  [default:\n",
+      "                                  hocomoco_v13]\n",
+      "  --device TEXT                   Device to use for attribution analysis.\n",
+      "  --genome TEXT                   Genome name or path to the genome fasta\n",
+      "                                  file.  [default: hg38]\n",
+      "  --help                          Show this message and exit.\n",
       "\u001b[0m"
      ]
     }
@@ -140,24 +530,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:25:56.149231Z",
-     "iopub.status.busy": "2025-11-21T06:25:56.149075Z",
-     "iopub.status.idle": "2025-11-21T06:26:54.297385Z",
-     "shell.execute_reply": "2025-11-21T06:26:54.296708Z"
-    }
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
+      "decima - INFO - Using device: 0\n",
+      "decima - INFO - Loading model v1_rep0 and metadata to compute attributions...\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...:  50%|█████████         | 1/2 [00:01<00:01,  1.48s/it]decima - WARNING - Gene BRD3 has low correlation with the model. Pearson: 0.3440624267844621. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.20s/it]\n",
+      "decima - INFO - Loading model and metadata to compute attributions...\n",
+      "Computing recursive seqlet calling...: 100%|█████| 2/2 [00:00<00:00, 717.59it/s]\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "\u001b[0m\u001b[0m\u001b[0m"
      ]
     },
     {
@@ -216,7 +606,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|                          | 0/2 [00:00<?, ?it/s]"
      ]
     },
@@ -224,7 +614,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:  50%|█████████         | 1/2 [00:02<00:02,  2.23s/it]"
      ]
     },
@@ -233,7 +623,7 @@
      "output_type": "stream",
      "text": [
       "decima - WARNING - Gene BRD3 has low correlation with the model. Pearson: 0.3440624267844621. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\r\n",
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.33s/it]"
      ]
     },
@@ -241,7 +631,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.49s/it]\r\n"
      ]
     },
@@ -286,8 +676,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Computing recursive seqlet calling...:   0%|              | 0/2 [00:00<?, ?it/s]\r",
+      "\r\n",
+      "Computing recursive seqlet calling...:   0%|              | 0/2 [00:00<?, ?it/s]\r\n",
       "Computing recursive seqlet calling...: 100%|█████| 2/2 [00:00<00:00, 459.62it/s]\r\n"
      ]
     },
@@ -348,70 +738,41 @@
     }
    ],
    "source": [
-    "! decima attributions --model v1_rep0 --genes \"SPI1,BRD3\" --tasks \"cell_type == 'classical monocyte'\" --output-prefix example/output_classical_monoctypes"
+    "! decima attributions --model v1_rep0 --genes \"SPI1,BRD3\" --tasks \"cell_type == 'classical monocyte'\" --output-prefix example/output_classical_monocytes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:54.299161Z",
-     "iopub.status.busy": "2025-11-21T06:26:54.299001Z",
-     "iopub.status.idle": "2025-11-21T06:26:54.432158Z",
-     "shell.execute_reply": "2025-11-21T06:26:54.431663Z"
-    }
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "example/output_classical_monoctypes_0.attributions.bigwig\r\n",
-      "example/output_classical_monoctypes_0.attributions.h5\r\n",
-      "example/output_classical_monoctypes_0.warnings.qc.log\r\n",
-      "example/output_classical_monoctypes_1.attributions.bigwig\r\n",
-      "example/output_classical_monoctypes_1.attributions.h5\r\n",
-      "example/output_classical_monoctypes_1.warnings.qc.log\r\n",
-      "example/output_classical_monoctypes.attributions.bigwig\r\n",
-      "example/output_classical_monoctypes.attributions.h5\r\n",
-      "example/output_classical_monoctypes.motifs.tsv\r\n",
-      "example/output_classical_monoctypes.seqlets.bed\r\n",
-      "example/output_classical_monoctypes.warnings.qc.log\r\n",
-      "\r\n",
-      "example/output_classical_monoctypes_plots:\r\n",
-      "BRD3.peaks.png\tBRD3_seqlogos  SPI1.peaks.png  SPI1_seqlogos\r\n"
+      "example/output_classical_monocytes.attributions.bigwig\n",
+      "example/output_classical_monocytes.attributions.h5\n",
+      "example/output_classical_monocytes.motifs.tsv\n",
+      "example/output_classical_monocytes.seqlets.bed\n",
+      "example/output_classical_monocytes.warnings.qc.log\n"
      ]
     }
    ],
    "source": [
-    "! ls example/output_classical_monoctypes*"
+    "! ls example/output_classical_monocytes*"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:54.433731Z",
-     "iopub.status.busy": "2025-11-21T06:26:54.433567Z",
-     "iopub.status.idle": "2025-11-21T06:26:54.564962Z",
-     "shell.execute_reply": "2025-11-21T06:26:54.564164Z"
-    }
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[b'SPI1' b'BRD3']\n",
-      "(2, 524288)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "(2, 524288)\n",
       "(2, 4, 524288)\n"
      ]
     }
@@ -419,7 +780,7 @@
    "source": [
     "import h5py\n",
     "\n",
-    "with h5py.File(\"example/output_classical_monoctypes.attributions.h5\", \"r\") as f:\n",
+    "with h5py.File(\"example/output_classical_monocytes.attributions.h5\", \"r\") as f:\n",
     "    print(f[\"genes\"][:])\n",
     "    print(f[\"sequence\"][:].shape)\n",
     "    print(f[\"attribution\"][:].shape)"
@@ -427,68 +788,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:54.566376Z",
-     "iopub.status.busy": "2025-11-21T06:26:54.566134Z",
-     "iopub.status.idle": "2025-11-21T06:26:54.699176Z",
-     "shell.execute_reply": "2025-11-21T06:26:54.698561Z"
-    }
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chr11  47152015  47152022  neg.SPI1@-29953  3.31894  .  -0.41688821464776993\r\n",
-      "chr11  47160163  47160167  neg.SPI1@-21805  3.33346  .  -0.2519867978990078\r\n",
-      "chr11  47160309  47160319  neg.SPI1@-21659  3.36046  .  -0.7482765801250935\r\n",
-      "chr11  47160323  47160333  neg.SPI1@-21645  3.35597  .  -0.9791450921911746\r\n",
-      "chr11  47165053  47165060  neg.SPI1@-16915  3.35769  .  -0.46799773909151554\r\n",
-      "chr11  47165593  47165606  pos.SPI1@-16375  3.41662  .  1.8055671770125628\r\n",
-      "chr11  47165642  47165653  pos.SPI1@-16326  3.37915  .  1.059839816763997\r\n",
-      "chr11  47165653  47165664  neg.SPI1@-16315  3.64033  .  -1.4299414344131947\r\n",
-      "chr11  47165664  47165670  neg.SPI1@-16304  3.36339  .  -0.4015889251604676\r\n",
-      "chr11  47165690  47165703  pos.SPI1@-16278  3.3902   .  1.1793060060590506\r\n"
+      "chr11  47152015  47152022  neg.SPI1@-29953  3.30838  .  -0.4167153239250183\n",
+      "chr11  47160163  47160167  neg.SPI1@-21805  3.33429  .  -0.2519679665565491\n",
+      "chr11  47160309  47160319  neg.SPI1@-21659  3.35953  .  -0.7482608761638403\n",
+      "chr11  47160323  47160333  neg.SPI1@-21645  3.35119  .  -0.9790758623275906\n",
+      "chr11  47165053  47165060  neg.SPI1@-16915  3.34766  .  -0.46816462464630604\n",
+      "chr11  47165593  47165606  pos.SPI1@-16375  3.41696  .  1.7989069446921349\n",
+      "chr11  47165642  47165653  pos.SPI1@-16326  3.38166  .  1.061532354913652\n",
+      "chr11  47165653  47165664  neg.SPI1@-16315  3.64029  .  -1.426463060081005\n",
+      "chr11  47165664  47165670  neg.SPI1@-16304  3.36199  .  -0.40094030275940895\n",
+      "chr11  47165690  47165703  pos.SPI1@-16278  3.39916  .  1.1820420632138848\n"
      ]
     }
    ],
    "source": [
-    "! head  example/output_classical_monoctypes.seqlets.bed | column -t -s $'\\t' "
+    "! head example/output_classical_monocytes.seqlets.bed | column -t -s $'\\t' "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:54.700835Z",
-     "iopub.status.busy": "2025-11-21T06:26:54.700681Z",
-     "iopub.status.idle": "2025-11-21T06:26:54.831437Z",
-     "shell.execute_reply": "2025-11-21T06:26:54.830965Z"
-    }
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ZNF507.H13CORE.0.I.B      pos.BRD3@1374     165221  165230  +  9.005010962486267   0.0004997253417968743  CTCCTTCCC                0.0001575700912831558   -0.0002066142760199578   1381\r\n",
-      "PPARA.H13CORE.1.P.B       pos.BRD3@-61106   102728  102737  +  8.097402691841125   0.0004997253417968743  AAGAGGTGA                0.0009877644590435214   0.0027158458094883164    -61112\r\n",
-      "ZNF507.H13CORE.0.I.B      neg.BRD3@1388     165221  165230  +  9.005010962486267   0.0004997253417968743  CTCCTTCCC                0.0001575700912831558   -0.0002066142760199578   1381\r\n",
-      "ARNT.H13CORE.0.P.B        pos.BRD3@26580    190423  190432  +  8.84844446182251    0.0004997253417968755  GGACGTGTT                0.0001840576308798821   -0.00032131952384467405  26583\r\n",
-      "ZN394.H13CORE.0.P.C       pos.BRD3@573      164409  164428  +  6.16656231880188    0.0004997442047169893  GCCGCCGGAGCCGCGAGGC      0.0016583528068670268   0.003810155552068253     569\r\n",
-      "ZNF30.H13CORE.0.P.C       neg.BRD3@291      164117  164140  -  7.218540787696838   0.0004997881442250214  CGGGCGCCGAGCCCCGCCCCCGC  -0.0007085893436021212  -0.001097117428680497    277\r\n",
-      "NR1H4.H13CORE.1.P.B       pos.BRD3@194003   357832  357850  -  7.282587647438049   0.0004999181110179031  CCTTGGAGGCAGTGACTC       0.0006487710052169859   0.0014354905397428942    193992\r\n",
-      "CGGBP1.H13CORE.0.PSGIB.A  neg.BRD3@614      164462  164473  -  9.182251572608948   0.0004999637603759763  GGGGCGGCGGG              4.89058068276129e-05    0.000644476072918215     622\r\n",
-      "KLF7.H13CORE.0.P.B        neg.BRD3@-102394  61443   61453   -  15.217368483543396                         CCCCGCCCCC               -0.0013154596599633805  -0.0038895047854197937   -102397\r\n",
-      "KLF7.H13CORE.0.P.B        neg.BRD3@291      164128  164138  -  15.217368483543396                         CCCCGCCCCC               -0.0009656054913648404  -0.0033124240223047752   288\r\n"
+      "ZNF507.H13CORE.0.I.B      pos.BRD3@1374     165221  165230  +  9.005010962486267   0.0004997253417968743  CTCCTTCCC                0.00015730424072696932  -0.00020671025662733617  1381\n",
+      "ZNF507.H13CORE.0.I.B      neg.BRD3@1388     165221  165230  +  9.005010962486267   0.0004997253417968743  CTCCTTCCC                0.00015730424072696932  -0.00020671025662733617  1381\n",
+      "PPARA.H13CORE.1.P.B       pos.BRD3@-61106   102728  102737  +  8.097402691841125   0.0004997253417968743  AAGAGGTGA                0.0009838229317311845   0.002703055938096474     -61112\n",
+      "ARNT.H13CORE.0.P.B        pos.BRD3@26580    190423  190432  +  8.84844446182251    0.0004997253417968755  GGACGTGTT                0.00020905238973985737  -0.000270835923655088    26583\n",
+      "ZN394.H13CORE.0.P.C       pos.BRD3@573      164409  164428  +  6.16656231880188    0.0004997442047169893  GCCGCCGGAGCCGCGAGGC      0.001663087628625554    0.0038150285899866657    569\n",
+      "ZNF30.H13CORE.0.P.C       neg.BRD3@291      164117  164140  -  7.218540787696838   0.0004997881442250214  CGGGCGCCGAGCCCCGCCCCCGC  -0.0007074664841322532  -0.0010954294541696311   277\n",
+      "NR1H4.H13CORE.1.P.B       pos.BRD3@194003   357832  357850  -  7.282587647438049   0.0004999181110179031  CCTTGGAGGCAGTGACTC       0.000649148471792513    0.0014365694512530167    193992\n",
+      "CGGBP1.H13CORE.0.PSGIB.A  neg.BRD3@614      164462  164473  -  9.182251572608948   0.0004999637603759763  GGGGCGGCGGG              5.052001308120618e-05   0.000652450375670573     622\n",
+      "KLF7.H13CORE.0.P.B        neg.BRD3@-102394  61443   61453   -  15.217368483543396                         CCCCGCCCCC               -0.0013158029003534466  -0.00389078689764279     -102397\n",
+      "KLF7.H13CORE.0.P.B        neg.BRD3@291      164128  164138  -  15.217368483543396                         CCCCGCCCCC               -0.0009646239963331027  -0.0033102193020789162   288\n"
      ]
     }
    ],
    "source": [
-    "! tail example/output_classical_monoctypes.motifs.tsv | column -t -s $'\\t' "
+    "! tail example/output_classical_monocytes.motifs.tsv | column -t -s $'\\t' "
    ]
   },
   {
@@ -500,26 +847,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:54.833081Z",
-     "iopub.status.busy": "2025-11-21T06:26:54.832932Z",
-     "iopub.status.idle": "2025-11-21T06:26:54.962911Z",
-     "shell.execute_reply": "2025-11-21T06:26:54.962435Z"
-    }
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "head: cannot open 'output_classical_monoctypes.warnings.qc.log' for reading: No such file or directory\r\n"
+      "warning: Gene BRD3 has low correlation with the model. Pearson: 0.3440624267844621. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n"
      ]
     }
    ],
    "source": [
-    "! head output_classical_monoctypes.warnings.qc.log"
+    "! head example/output_classical_monocytes.warnings.qc.log"
    ]
   },
   {
@@ -550,32 +890,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:54.964665Z",
-     "iopub.status.busy": "2025-11-21T06:26:54.964508Z",
-     "iopub.status.idle": "2025-11-21T06:27:16.938452Z",
-     "shell.execute_reply": "2025-11-21T06:27:16.937801Z"
-    }
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "decima - INFO - Using device: 0\r\n",
-      "decima - INFO - Loading model v1_rep0 and metadata to compute attributions...\r\n"
+      "decima - INFO - Using device: 0\n",
+      "decima - INFO - Loading model v1_rep0 and metadata to compute attributions...\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...:  50%|█████████         | 1/2 [00:01<00:01,  1.53s/it]decima - WARNING - Gene BRD3 has low correlation with the model. Pearson: 0.3440624267844621. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.24s/it]\n",
+      "\u001b[0m"
      ]
     },
     {
@@ -620,7 +948,7 @@
      "output_type": "stream",
      "text": [
       "/home/celikm5/Projects/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\r\n",
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|                          | 0/2 [00:00<?, ?it/s]"
      ]
     },
@@ -628,7 +956,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:  50%|█████████         | 1/2 [00:01<00:01,  1.33s/it]"
      ]
     },
@@ -637,7 +965,7 @@
      "output_type": "stream",
      "text": [
       "decima - WARNING - Gene BRD3 has low correlation with the model. Pearson: 0.3440624267844621. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\r\n",
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.04it/s]"
      ]
     },
@@ -645,7 +973,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.04s/it]\r\n"
      ]
     },
@@ -658,37 +986,24 @@
     }
    ],
    "source": [
-    "! decima attributions-predict --model v1_rep0 --genes \"SPI1,BRD3\" --tasks \"cell_type == 'classical monocyte'\" --output-prefix example/output_classical_monoctypes_0"
+    "! decima attributions-predict --model v1_rep0 --genes \"SPI1,BRD3\" --tasks \"cell_type == 'classical monocyte'\" --output-prefix example/output_classical_monocytes_0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:27:16.940364Z",
-     "iopub.status.busy": "2025-11-21T06:27:16.940205Z",
-     "iopub.status.idle": "2025-11-21T06:27:38.456219Z",
-     "shell.execute_reply": "2025-11-21T06:27:38.455612Z"
-    }
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "decima - INFO - Using device: 0\r\n",
-      "decima - INFO - Loading model v1_rep1 and metadata to compute attributions...\r\n"
+      "decima - INFO - Using device: 0\n",
+      "decima - INFO - Loading model v1_rep1 and metadata to compute attributions...\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...:  50%|█████████         | 1/2 [00:01<00:01,  1.48s/it]decima - WARNING - Gene BRD3 has low correlation with the model. Pearson: 0.3440624267844621. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.20s/it]\n",
+      "\u001b[0m"
      ]
     },
     {
@@ -733,7 +1048,7 @@
      "output_type": "stream",
      "text": [
       "/home/celikm5/Projects/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\r\n",
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|                          | 0/2 [00:00<?, ?it/s]"
      ]
     },
@@ -741,7 +1056,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:  50%|█████████         | 1/2 [00:01<00:01,  1.37s/it]"
      ]
     },
@@ -750,7 +1065,7 @@
      "output_type": "stream",
      "text": [
       "decima - WARNING - Gene BRD3 has low correlation with the model. Pearson: 0.3440624267844621. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\r\n",
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.01it/s]"
      ]
     },
@@ -758,7 +1073,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.07s/it]\r\n"
      ]
     },
@@ -771,43 +1086,32 @@
     }
    ],
    "source": [
-    "! decima attributions-predict --model v1_rep1 --genes \"SPI1,BRD3\" --tasks \"cell_type == 'classical monocyte'\" --output-prefix example/output_classical_monoctypes_1"
+    "! decima attributions-predict --model v1_rep1 --genes \"SPI1,BRD3\" --tasks \"cell_type == 'classical monocyte'\" --output-prefix example/output_classical_monocytes_1"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This cell runs the recursive seqlet calling step of the Decima attribution pipeline. It takes the attributions from two models (model 0 and model 1) for the genes SPI1 and BRD3in classical monocytes, and calls significant seqlets (regions with high attribution)."
+    "This cell runs the recursive seqlet calling step of the Decima attribution pipeline. It takes the attributions from two models (model 0 and model 1) for the genes SPI1 and BRD3 in classical monocytes, and calls significant seqlets (regions with high attribution)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:27:38.458079Z",
-     "iopub.status.busy": "2025-11-21T06:27:38.457920Z",
-     "iopub.status.idle": "2025-11-21T06:28:18.744808Z",
-     "shell.execute_reply": "2025-11-21T06:28:18.743937Z"
-    }
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "decima - INFO - Loading model and metadata to compute attributions...\r\n"
+      "decima - INFO - Loading model and metadata to compute attributions...\n",
+      "decima - INFO - No genes provided, using all 2 genes in the attribution files.\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "Computing recursive seqlet calling...: 100%|█████| 2/2 [00:00<00:00, 851.38it/s]\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "\u001b[0m\u001b[0m\u001b[0m"
      ]
     },
     {
@@ -858,8 +1162,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Computing recursive seqlet calling...:   0%|              | 0/2 [00:00<?, ?it/s]\r",
+      "\r\n",
+      "Computing recursive seqlet calling...:   0%|              | 0/2 [00:00<?, ?it/s]\r\n",
       "Computing recursive seqlet calling...: 100%|█████| 2/2 [00:00<00:00, 855.46it/s]\r\n"
      ]
     },
@@ -920,7 +1224,7 @@
     }
    ],
    "source": [
-    "! decima attributions-recursive-seqlet-calling --attributions \"example/output_classical_monoctypes_0.attributions.h5,example/output_classical_monoctypes_1.attributions.h5\" --output-prefix example/output_classical_monoctypes"
+    "! decima attributions-recursive-seqlet-calling --attributions \"example/output_classical_monocytes_0.attributions.h5,example/output_classical_monocytes_1.attributions.h5\" --output-prefix example/output_classical_monocytes"
    ]
   },
   {
@@ -932,32 +1236,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:28:18.746943Z",
-     "iopub.status.busy": "2025-11-21T06:28:18.746747Z",
-     "iopub.status.idle": "2025-11-21T06:28:53.778877Z",
-     "shell.execute_reply": "2025-11-21T06:28:53.778211Z"
-    }
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Plotting attributions...:   0%|                           | 0/2 [00:00<?, ?it/s]"
+      "Plotting attributions...:   0%|                           | 0/2 [00:00<?, ?it/s]Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/plotnine/ggplot.py:623: PlotnineWarning: Saving 10 x 2 in image.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/plotnine/ggplot.py:624: PlotnineWarning: Filename: example/output_classical_monocytes_plots/SPI1.peaks.png\n",
+      "Plotting attributions...:  50%|█████████▌         | 1/2 [00:11<00:11, 11.88s/it]/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/plotnine/ggplot.py:623: PlotnineWarning: Saving 10 x 2 in image.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/plotnine/ggplot.py:624: PlotnineWarning: Filename: example/output_classical_monocytes_plots/BRD3.peaks.png\n",
+      "Plotting attributions...: 100%|███████████████████| 2/2 [00:20<00:00, 10.05s/it]\n",
+      "\u001b[0m"
      ]
     },
     {
@@ -994,7 +1286,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Plotting attributions...:  50%|█████████▌         | 1/2 [00:12<00:12, 12.95s/it]"
      ]
     },
@@ -1025,37 +1317,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Plotting attributions...: 100%|███████████████████| 2/2 [00:23<00:00, 11.76s/it]\r",
+      "\r\n",
+      "Plotting attributions...: 100%|███████████████████| 2/2 [00:23<00:00, 11.76s/it]\r\n",
       "Plotting attributions...: 100%|███████████████████| 2/2 [00:23<00:00, 11.94s/it]\r\n",
       "\u001b[0m"
      ]
     }
    ],
    "source": [
-    "! decima attributions-plot --output-prefix example/output_classical_monoctypes -g \"SPI1,BRD3\" --tss-distance 500"
+    "! decima attributions-plot --output-prefix example/output_classical_monocytes -g \"SPI1,BRD3\" --tss-distance 500"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:28:53.780962Z",
-     "iopub.status.busy": "2025-11-21T06:28:53.780763Z",
-     "iopub.status.idle": "2025-11-21T06:28:53.786794Z",
-     "shell.execute_reply": "2025-11-21T06:28:53.786352Z"
-    }
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1063,7 +1348,7 @@
    "source": [
     "from IPython.display import Image\n",
     "\n",
-    "Image(\"example/output_classical_monoctypes_plots/SPI1_seqlogos/SPI1@267.png\")"
+    "Image(\"example/output_classical_monocytes_plots/SPI1_seqlogos/SPI1@267.png\")"
    ]
   },
   {
@@ -1082,48 +1367,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:28:53.788330Z",
-     "iopub.status.busy": "2025-11-21T06:28:53.788190Z",
-     "iopub.status.idle": "2025-11-21T06:29:05.164957Z",
-     "shell.execute_reply": "2025-11-21T06:29:05.164317Z"
-    }
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Usage: decima query-cell [OPTIONS] [QUERY]\r\n",
-      "\r\n",
-      "  Query a cell using query string\r\n",
-      "\r\n",
-      "  Examples:\r\n",
-      "\r\n",
-      "      >>> decima query-cell 'cell_type == \"classical monocyte\"'     ...\r\n",
-      "\r\n",
-      "      >>> decima query-cell 'cell_type == \"classical monocyte\" and disease ==\r\n",
-      "      \"healthy\" and tissue == \"blood\"'     ...\r\n",
-      "\r\n",
-      "      >>> decima query-cell 'cell_type.str.contains(\"monocyte\") and disease ==\r\n",
-      "      \"healthy\"'     ...\r\n",
-      "\r\n",
-      "Options:\r\n",
-      "  --metadata TEXT  Path to the metadata anndata file or name of the model.\r\n",
-      "                   Default: ensemble.\r\n",
-      "  --help           Show this message and exit.\r\n",
+      "Usage: decima query-cell [OPTIONS] [QUERY]\n",
+      "\n",
+      "  Query a cell using query string\n",
+      "\n",
+      "  Examples:\n",
+      "\n",
+      "      >>> decima query-cell 'cell_type == \"classical monocyte\"'     ...\n",
+      "\n",
+      "      >>> decima query-cell 'cell_type == \"classical monocyte\" and disease ==\n",
+      "      \"healthy\" and tissue == \"blood\"'     ...\n",
+      "\n",
+      "      >>> decima query-cell 'cell_type.str.contains(\"monocyte\") and disease ==\n",
+      "      \"healthy\"'     ...\n",
+      "\n",
+      "Options:\n",
+      "  --metadata TEXT  Path to the metadata anndata file or name of the model.\n",
+      "                   Default: ensemble.\n",
+      "  --help           Show this message and exit.\n",
       "\u001b[0m"
      ]
     }
@@ -1141,31 +1409,201 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:29:05.166830Z",
-     "iopub.status.busy": "2025-11-21T06:29:05.166668Z",
-     "iopub.status.idle": "2025-11-21T06:29:20.089279Z",
-     "shell.execute_reply": "2025-11-21T06:29:20.088698Z"
-    }
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\r\n"
+      "          cell_type           tissue                            organ           disease                                         study                                 dataset      region  subregion  celltype_coarse  n_cells  total_counts        n_genes  size_factor         train_pearson       val_pearson         test_pearson\n",
+      "agg_4705  classical monocyte  alveolar system                   lung            COVID-19                                        GSE155249                             scimilarity  nan     nan                         7244     26544273.0          15325    34749.092791034054  0.946616874183219   0.8437000068912937  0.8506571540216992\n",
+      "agg_4706  classical monocyte  alveolar system                   lung            healthy                                         GSE155249                             scimilarity  nan     nan                         72       218105.0            9142     30484.31888978114   0.9102228263646758  0.8083487523192785  0.8047828694155461\n",
+      "agg_4707  classical monocyte  ampulla of uterine tube           fallopian tube  healthy                                         fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         78       550950.0            9639     30719.377971431015  0.9077670011915634  0.8045070167513724  0.7896845423359651\n",
+      "agg_4708  classical monocyte  aorta                             vasculature     Abdominal Aortic Aneurysm                       GSE166676                             scimilarity  nan     nan                         432      1091075.0           11192    32981.443348717905  0.9389265854768138  0.8357299205241656  0.830575965756882\n",
+      "agg_4709  classical monocyte  aorta                             vasculature     healthy                                         GSE166676                             scimilarity  nan     nan                         25       162858.0            8859     31216.275954364824  0.8819013257206973  0.7821403055329706  0.7646999711802146\n",
+      "agg_4710  classical monocyte  apex of heart                     heart           healthy                                         b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         397      1226515.0           12369    32022.563851814968  0.9469178617442242  0.8326145310572417  0.8365506153530168\n",
+      "agg_4711  classical monocyte  blood                             blood           COVID-19                                        03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         17462    78882609.0          15711    33080.17541357136   0.9536210517623883  0.8539379752673611  0.8485800714004562\n",
+      "agg_4712  classical monocyte  blood                             blood           COVID-19                                        7d7cabfd-1d1f-40af-96b7-26a0825a306d  scimilarity  nan     nan                         141914   659177004.0         15175    32282.29230923367   0.9575085257562032  0.8570056960758388  0.8507770895392532\n",
+      "agg_4713  classical monocyte  blood                             blood           COVID-19                                        GSE154567                             scimilarity  nan     nan                         8613     40239000.0          16023    34450.628692057784  0.9605287026438375  0.8551842525202262  0.8491670381176235\n",
+      "agg_4714  classical monocyte  blood                             blood           COVID-19                                        GSE158034                             scimilarity  nan     nan                         35       91390.0             7372     27446.425385592618  0.8476138307738649  0.7606096001026369  0.7256993661246048\n",
+      "agg_4715  classical monocyte  blood                             blood           COVID-19                                        GSE161918                             scimilarity  nan     nan                         163244   1023475761.0        15929    31151.84947891148   0.9361102289470363  0.8261601916328626  0.8168421752771801\n",
+      "agg_4716  classical monocyte  blood                             blood           COVID-19                                        GSE163668                             scimilarity  nan     nan                         8399     55036800.0          15792    33644.10626885235   0.9571638472088531  0.8529462339847145  0.8506441761860545\n",
+      "agg_4717  classical monocyte  blood                             blood           COVID-19                                        GSE166992                             scimilarity  nan     nan                         2238     12283507.0          14186    32596.636302802952  0.9567710210039843  0.8531485173368566  0.8416173697367906\n",
+      "agg_4718  classical monocyte  blood                             blood           COVID-19                                        ddfad306-714d-4cc0-9985-d9072820c530  scimilarity  nan     nan                         61002    230056884.0         16484    32520.14418628346   0.9487335053479237  0.8533686239486711  0.8454541123444707\n",
+      "agg_4719  classical monocyte  blood                             blood           COVID-19                                        eb735cc9-d0a7-48fa-b255-db726bf365af  scimilarity  nan     nan                         19777    105875381.0         15812    32330.088619084574  0.9558882745902155  0.8545238316898663  0.8468877639468763\n",
+      "agg_4720  classical monocyte  blood                             blood           HIV enteropathy                                 GSE157829                             scimilarity  nan     nan                         491      1449812.0           12290    33110.90004135926   0.9412108394642186  0.8352699509238034  0.8345507070277177\n",
+      "agg_4721  classical monocyte  blood                             blood           Myelofibrosis                                   GSE117824                             scimilarity  nan     nan                         357      1492491.0           11548    32726.985198452294  0.9446223529088382  0.8417521390049872  0.8328218073658378\n",
+      "agg_4722  classical monocyte  blood                             blood           NA                                              GSE132950                             scimilarity  nan     nan                         146      784054.0            10913    30417.15641845661   0.9276395863920666  0.8264978172767997  0.8176327551177259\n",
+      "agg_4723  classical monocyte  blood                             blood           NA                                              GSE135325                             scimilarity  nan     nan                         232      633533.0            11129    31159.105128910356  0.9369963391148282  0.8254811186623798  0.8207578599532835\n",
+      "agg_4724  classical monocyte  blood                             blood           NA                                              GSE150233                             scimilarity  nan     nan                         1141     2453545.0           12228    32204.245569759012  0.9354773292749718  0.8333534679658088  0.8202743631285762\n",
+      "agg_4725  classical monocyte  blood                             blood           NA                                              GSE151310                             scimilarity  nan     nan                         48       151358.0            8028     27001.118740317568  0.8873812787091045  0.7886356061906991  0.766461694552445\n",
+      "agg_4726  classical monocyte  blood                             blood           NA                                              GSE164378                             scimilarity  nan     nan                         54305    476237982.0         17463    34023.11682209347   0.9636663701487779  0.856267291847072   0.8496477594095655\n",
+      "agg_4727  classical monocyte  blood                             blood           NA                                              GSE164402                             scimilarity  nan     nan                         6577     33889420.0          14992    33855.14311643263   0.9502216042319906  0.846017695872854   0.8447747394204608\n",
+      "agg_4728  classical monocyte  blood                             blood           Sezary's disease                                GSE122703                             scimilarity  nan     nan                         35       148650.0            8487     29592.979037498706  0.8928094999389883  0.7911806688728295  0.7911936593448785\n",
+      "agg_4729  classical monocyte  blood                             blood           dengue disease                                  GSE145307                             scimilarity  nan     nan                         785      7639702.0           13722    33610.52078618725   0.9561427618691068  0.8544883780028308  0.8514781068765508\n",
+      "agg_4730  classical monocyte  blood                             blood           dengue disease                                  GSE154386                             scimilarity  nan     nan                         19173    143929741.0         16877    34242.50262506596   0.9586193824399223  0.8509705295166231  0.8546685528097621\n",
+      "agg_4731  classical monocyte  blood                             blood           drug hypersensitivity syndrome                  GSE132802                             scimilarity  nan     nan                         1269     7314697.0           13270    32574.34811388645   0.9570929839341253  0.8466339050741839  0.8442788242172967\n",
+      "agg_4732  classical monocyte  blood                             blood           fibrosis                                        GSE136103                             scimilarity  nan     nan                         1774     5003888.0           13389    31155.271000486402  0.9562933985421416  0.8435982250231042  0.8386367834560556\n",
+      "agg_4733  classical monocyte  blood                             blood           healthy                                         03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         32464    109280914.0         16158    33646.02843110038   0.9568728712803031  0.8545324533535094  0.8487445540580735\n",
+      "agg_4734  classical monocyte  blood                             blood           healthy                                         436154da-bcf1-4130-9c8b-120ff9a888f2  scimilarity  nan     nan                         76800    206490628.0         16683    30736.453324546856  0.955313650235467   0.8494127267867799  0.84210567312908\n",
+      "agg_4735  classical monocyte  blood                             blood           healthy                                         5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         1044     3638976.0           12306    30542.8977364245    0.9465384578921433  0.851991795617166   0.8308191715256182\n",
+      "agg_4736  classical monocyte  blood                             blood           healthy                                         DS000010023                           scimilarity  nan     nan                         243      362606.0            8414     25201.261024165953  0.8865446516098515  0.7621391376670988  0.7681818769796727\n",
+      "agg_4737  classical monocyte  blood                             blood           healthy                                         GSE122703                             scimilarity  nan     nan                         18       83417.0             7546     28194.725173315186  0.859225612396475   0.7640890699253299  0.7443681539461423\n",
+      "agg_4738  classical monocyte  blood                             blood           healthy                                         GSE130117                             scimilarity  nan     nan                         2017     7130588.0           13535    33078.53692191542   0.9553673450160365  0.851402109626239   0.8385936100758409\n",
+      "agg_4739  classical monocyte  blood                             blood           healthy                                         GSE132802                             scimilarity  nan     nan                         1601     9955248.0           13132    32063.630951743195  0.9478882791739611  0.8391025143866828  0.8303465877530952\n",
+      "agg_4740  classical monocyte  blood                             blood           healthy                                         GSE139324                             scimilarity  nan     nan                         2333     8331045.0           13985    31135.881287246768  0.9608208780142045  0.8473885992448625  0.8432790193723467\n",
+      "agg_4741  classical monocyte  blood                             blood           healthy                                         GSE145809                             scimilarity  nan     nan                         69       245221.0            8962     29135.67197629852   0.8825701041728526  0.7811799267734735  0.7818647625179129\n",
+      "agg_4742  classical monocyte  blood                             blood           healthy                                         GSE149313                             scimilarity  nan     nan                         2420     6974751.0           13143    29560.496854576566  0.9574598613513423  0.8505290963248237  0.8379199735887167\n",
+      "agg_4743  classical monocyte  blood                             blood           healthy                                         GSE153421                             scimilarity  nan     nan                         3691     15561725.0          14569    34377.465875728165  0.9636686704566925  0.8576434473562725  0.8511814190737197\n",
+      "agg_4744  classical monocyte  blood                             blood           healthy                                         GSE156989                             scimilarity  nan     nan                         13554    160011485.0         16915    34135.439844737564  0.9640667421350761  0.8577967800377495  0.8517975138366085\n",
+      "agg_4745  classical monocyte  blood                             blood           healthy                                         GSE157829                             scimilarity  nan     nan                         1619     6957811.0           13507    30199.39288988673   0.9484019976492215  0.8436979316400604  0.8347196616710685\n",
+      "agg_4746  classical monocyte  blood                             blood           healthy                                         GSE159113                             scimilarity  nan     nan                         1025     6298250.0           12083    27477.50809897617   0.9078020151513733  0.8121457150205226  0.7980372877810575\n",
+      "agg_4747  classical monocyte  blood                             blood           healthy                                         GSE161329                             scimilarity  nan     nan                         5654     25653579.0          14349    28848.0539929647    0.9549801428956252  0.8450430950674043  0.8406188789518544\n",
+      "agg_4748  classical monocyte  blood                             blood           healthy                                         GSE161738                             scimilarity  nan     nan                         2676     13801473.0          12825    33337.477050230416  0.9541962906717452  0.8512846409758499  0.8485408028961247\n",
+      "agg_4749  classical monocyte  blood                             blood           healthy                                         GSE163668                             scimilarity  nan     nan                         2644     10486314.0          14049    33786.96584264489   0.9597801578342394  0.8560775485935677  0.8512149509551471\n",
+      "agg_4750  classical monocyte  blood                             blood           healthy                                         GSE166992                             scimilarity  nan     nan                         7501     28033216.0          15079    33455.367364577316  0.9622273594219685  0.8558958139235102  0.8495571689751152\n",
+      "agg_4751  classical monocyte  blood                             blood           healthy                                         GSE167363                             scimilarity  nan     nan                         3135     14722635.0          14375    29977.24002819913   0.942417448875388   0.8368071803109702  0.8258536430202982\n",
+      "agg_4752  classical monocyte  blood                             blood           healthy                                         GSE168710                             scimilarity  nan     nan                         16484    104881872.0         16223    34107.336261357574  0.9398282119039322  0.8424821834537695  0.8372971004604842\n",
+      "agg_4753  classical monocyte  blood                             blood           healthy                                         GSE168732                             scimilarity  nan     nan                         770      2548822.0           12508    33411.30103713399   0.9552513581030765  0.8508279875038706  0.847461536110767\n",
+      "agg_4754  classical monocyte  blood                             blood           healthy                                         b0cf0afa-ec40-4d65-b570-ed4ceacc6813  scimilarity  nan     nan                         40975    300555227.0         15784    35938.85772500803   0.9622425892039956  0.853424173800979   0.8508714303589978\n",
+      "agg_4755  classical monocyte  blood                             blood           healthy                                         ddfad306-714d-4cc0-9985-d9072820c530  scimilarity  nan     nan                         8827     36073928.0          15131    33208.591584008376  0.9546118779961532  0.8543086616569785  0.8462739374830107\n",
+      "agg_4756  classical monocyte  blood                             blood           intracranial hypotension                        GSE138266                             scimilarity  nan     nan                         2503     9675804.0           14485    30160.767605621222  0.9452052724479383  0.8423537848756032  0.8326629487875993\n",
+      "agg_4757  classical monocyte  blood                             blood           mucocutaneous lymph node syndrome               GSE168732                             scimilarity  nan     nan                         5745     25930751.0          14822    33366.18751424575   0.9564515409367231  0.8556431530577528  0.8540185868162636\n",
+      "agg_4758  classical monocyte  blood                             blood           multiple sclerosis                              GSE138266                             scimilarity  nan     nan                         3988     13926825.0          14991    31442.03464388843   0.9522779953120408  0.847799219646348   0.8382058078578654\n",
+      "agg_4759  classical monocyte  blood                             blood           non-alcoholic fatty liver disease               GSE136103                             scimilarity  nan     nan                         8306     29424841.0          15410    32004.200489375227  0.9619190264492873  0.8478709124980346  0.8451242436776344\n",
+      "agg_4760  classical monocyte  blood                             blood           rheumatoid arthritis                            GSE159117                             scimilarity  nan     nan                         834      4637566.0           12079    31364.847230552205  0.9356058277176598  0.8232134520999813  0.8176333921128414\n",
+      "agg_4761  classical monocyte  blood                             blood           septic shock                                    GSE167363                             scimilarity  nan     nan                         3860     51041813.0          15830    31688.79561595612   0.948652055959824   0.8541736693569211  0.8427375237296424\n",
+      "agg_4762  classical monocyte  blood                             blood           systemic lupus erythematosus                    436154da-bcf1-4130-9c8b-120ff9a888f2  scimilarity  nan     nan                         200468   516575809.0         16896    30011.373010792136  0.9562030923644677  0.8480520393236465  0.844052374052952\n",
+      "agg_4763  classical monocyte  blood                             blood           systemic lupus erythematosus                    GSE142016                             scimilarity  nan     nan                         8268     22146620.0          14873    30889.72528098081   0.9588937962174496  0.8480448150326806  0.8395981302528143\n",
+      "agg_4764  classical monocyte  blood                             blood           systemic lupus erythematosus                    GSE153765                             scimilarity  nan     nan                         42       109982.0            7500     27335.812367044335  0.8566470004710053  0.7665719714665945  0.7374607536624445\n",
+      "agg_4765  classical monocyte  blood                             blood           systemic lupus erythematosus                    GSE156989                             scimilarity  nan     nan                         30367    310637290.0         17082    33485.308563356346  0.9623402060903008  0.8532487075078466  0.8473526649094757\n",
+      "agg_4766  classical monocyte  blood                             blood           thrombocytopenia                                GSE149313                             scimilarity  nan     nan                         2724     15059814.0          14328    30599.80301260898   0.9543473550386421  0.8520722945129096  0.8417995728182829\n",
+      "agg_4767  classical monocyte  bone                              bone            Langerhans Cell Histiocytosis                   GSE133704                             scimilarity  nan     nan                         439      1404680.0           11388    30817.807833507268  0.9358504157466769  0.830348562008033   0.826566269904344\n",
+      "agg_4769  classical monocyte  bone marrow                       bone marrow     NA                                              GSE162692                             scimilarity  nan     nan                         1234     4757721.0           13466    31707.380952189662  0.953852620063789   0.8503857428588029  0.8377674131460707\n",
+      "agg_4770  classical monocyte  bone marrow                       bone marrow     essential thrombocythemia                       GSE117824                             scimilarity  nan     nan                         1649     7825780.0           13487    32454.468003620656  0.9503614540027875  0.8479601234457582  0.838408163377457\n",
+      "agg_4772  classical monocyte  bone marrow                       bone marrow     healthy                                         GSE132509                             scimilarity  nan     nan                         610      2315570.0           12950    31768.06513427212   0.95159369508558    0.8517118261701931  0.836658919433696\n",
+      "agg_4773  classical monocyte  bone marrow                       bone marrow     healthy                                         GSE154109                             scimilarity  nan     nan                         531      1431388.0           11793    31377.450948003392  0.9490546933955852  0.8431566630120637  0.8370883160295727\n",
+      "agg_4774  classical monocyte  bone marrow                       bone marrow     healthy                                         GSE163278                             scimilarity  nan     nan                         1119     3970394.0           13361    32081.93302956569   0.9620394897163868  0.8531148861215617  0.8426785397396367\n",
+      "agg_4775  classical monocyte  bone marrow                       bone marrow     healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         151      8025584.0           12883    29444.78352863075   0.8440608178227607  0.740574910750325   0.7417328577454956\n",
+      "agg_4776  classical monocyte  bone marrow                       bone marrow     monoclonal gammopathy                           GSE163278                             scimilarity  nan     nan                         1010     3124102.0           12959    30344.72094719757   0.9581906874137958  0.8503948562041261  0.8391672246132192\n",
+      "agg_4777  classical monocyte  breast                            breast          healthy                                         GSE164898                             scimilarity  nan     nan                         136      641471.0            12971    34463.52724138501   0.9163324788498406  0.8116274576633968  0.7978555908123931\n",
+      "agg_4778  classical monocyte  breast                            breast          healthy                                         c9706a92-0e5f-46c1-96d8-20e42467f287  scimilarity  nan     nan                         98       1444245.0           13491    30678.263421880285  0.9165520953567395  0.8162053142576849  0.7994301225229256\n",
+      "agg_4779  classical monocyte  bronchus                          airway          COVID-19                                        03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         104      270108.0            8816     27933.501359354133  0.8884582873892427  0.7928474093439662  0.786626157931558\n",
+      "agg_4780  classical monocyte  bronchus                          airway          COVID-19                                        GSE168215                             scimilarity  nan     nan                         90       217928.0            8444     27417.845704249117  0.880738805029126   0.7855823736726739  0.7798557380738542\n",
+      "agg_4782  classical monocyte  bronchus                          airway          healthy                                         GSE158127                             scimilarity  nan     nan                         158      1158198.0           12643    34764.50196701077   0.9364512338084163  0.8259291909369686  0.8266638555276521\n",
+      "agg_4783  classical monocyte  cardiac muscle of left ventricle  heart           healthy                                         GSE156703                             scimilarity  nan     nan                         13       116181.0            9463     35695.66320276271   0.8542740960069863  0.7515621053395214  0.7561639038477878\n",
+      "agg_4784  classical monocyte  carotid artery segment            vasculature     atherosclerosis                                 GSE155512                             scimilarity  nan     nan                         58       515211.0            10839    32837.84505237503   0.9343565353773022  0.8190650931322585  0.8202426969221358\n",
+      "agg_4785  classical monocyte  caudate lobe of liver             liver           healthy                                         44531dd9-1388-4416-a117-af0a99de2294  scimilarity  nan     nan                         238      730016.0            11505    31342.386314731422  0.9217674983890346  0.8140551552218395  0.8040417787954989\n",
+      "agg_4786  classical monocyte  cortex of kidney                  kidney          healthy                                         120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         79       323010.0            10939    32378.76683324232   0.9028856137251035  0.7978822439778066  0.7839454035009307\n",
+      "agg_4787  classical monocyte  cortex of kidney                  kidney          healthy                                         a98b828a-622a-483a-80e0-15703678befd  scimilarity  nan     nan                         91       477355.0            10898    32358.068865763344  0.9328436291917394  0.8237810319569842  0.8195391931798526\n",
+      "agg_4789  classical monocyte  digestive tract                   gut             healthy                                         DS000011665                           scimilarity  nan     nan                         347      1679116.0           12155    33347.55517047197   0.9422556928648441  0.8417267634096297  0.84018452536733\n",
+      "agg_4790  classical monocyte  exocrine pancreas                 pancreas        healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         821      7824069.0           14847    36135.64587109593   0.9493709998172055  0.8440837716099078  0.8410246939313819\n",
+      "agg_4791  classical monocyte  fallopian tube                    fallopian tube  healthy                                         fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         131      734093.0            11240    33115.640434504094  0.9359103734457376  0.8339026306142181  0.8225901799509813\n",
+      "agg_4792  classical monocyte  fimbria of uterine tube           fallopian tube  healthy                                         fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         34       209362.0            7663     27635.733860508328  0.8560416684135254  0.7382997749370328  0.7459235366949488\n",
+      "agg_4794  classical monocyte  gingiva                           mouth           periodontitis                                   GSE152042                             scimilarity  nan     nan                         198      879477.0            11312    32107.813302914532  0.9416262876541264  0.8333723697695014  0.8279530215775117\n",
+      "agg_4795  classical monocyte  head of femur                     bone            healthy                                         GSE169396                             scimilarity  nan     nan                         450      3669304.0           13216    33082.323604222154  0.9529417082022753  0.8522346343107771  0.8359032081996703\n",
+      "agg_4797  classical monocyte  heart left ventricle              heart           NA                                              ENCODE                                scimilarity  nan     nan                         50       138407.30523254164  11428    41105.63651890687   0.8614790128015358  0.7843765107548858  0.7889308929671582\n",
+      "agg_4798  classical monocyte  heart left ventricle              heart           healthy                                         b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         192      585001.0            11159    31422.874036870588  0.9363985001217598  0.8226438123601741  0.8283173244446851\n",
+      "agg_4799  classical monocyte  heart right ventricle             heart           healthy                                         b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         316      936263.0            11904    32002.67227624691   0.9425900348990802  0.8306128730459813  0.8308116461021977\n",
+      "agg_4800  classical monocyte  ileum                             gut             Crohn's disease                                 17481d16-ee44-49e5-bcf0-28c0780d8c4a  scimilarity  nan     nan                         76       311515.0            9984     29687.611679190355  0.9103310804624617  0.8063080284939284  0.7916226068478351\n",
+      "agg_4801  classical monocyte  ileum                             gut             Crohn's disease                                 DS000011665                           scimilarity  nan     nan                         119      298206.0            8021     26013.286557459236  0.880272438867354   0.7572099232100128  0.7459713937143965\n",
+      "agg_4802  classical monocyte  inferior nasal concha             bone            chronic rhinosinusitis with nasal polyps        GSE156285                             scimilarity  nan     nan                         241      1048981.0           12463    35082.083353928334  0.9475981193848912  0.8330375982148592  0.833114773003936\n",
+      "agg_4803  classical monocyte  interventricular septum           heart           healthy                                         b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         442      1322725.0           12418    32235.5434681197    0.94751399102473    0.8340623939411483  0.8353858365852226\n",
+      "agg_4804  classical monocyte  isthmus of fallopian tube         fallopian tube  healthy                                         fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         62       318330.0            8642     29768.198512126502  0.8846027131590791  0.784220371073871   0.7739277341448829\n",
+      "agg_4805  classical monocyte  kidney                            kidney          NA                                              GSE145927                             scimilarity  nan     nan                         1789     6957949.0           15145    34853.400215205314  0.9586386429540185  0.8579622487738222  0.8524123833700197\n",
+      "agg_4806  classical monocyte  kidney                            kidney          acute kidney failure                            bcb61471-2a44-4d00-a0af-ff085512674c  scimilarity  nan     nan                         587      1589224.0           12335    32471.78147434854   0.9513927341618255  0.84278469020191    0.8402011848101985\n",
+      "agg_4807  classical monocyte  kidney                            kidney          chronic kidney disease                          bcb61471-2a44-4d00-a0af-ff085512674c  scimilarity  nan     nan                         134      440788.0            10831    32410.84600407974   0.9323603662663799  0.8153954443953603  0.8190416636069936\n",
+      "agg_4808  classical monocyte  kidney                            kidney          healthy                                         120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         762      4034828.0           14946    34015.00816823295   0.9520640694425091  0.848205266473767   0.836085027208869\n",
+      "agg_4809  classical monocyte  kidney                            kidney          healthy                                         DS000010415                           scimilarity  nan     nan                         55       127079.0            8055     27206.419037355434  0.8216238135756493  0.7570847030479543  0.72524174726152\n",
+      "agg_4810  classical monocyte  kidney                            kidney          healthy                                         GSE140989                             scimilarity  nan     nan                         174      563438.0            11016    29887.593299155575  0.914390252807459   0.8069762795735104  0.8086759072557722\n",
+      "agg_4811  classical monocyte  left cardiac atrium               heart           NA                                              ENCODE                                scimilarity  nan     nan                         59       225070.96128814947  12831    43727.214042795575  0.8938217272345973  0.8048974803645641  0.8168195119621123\n",
+      "agg_4812  classical monocyte  left cardiac atrium               heart           healthy                                         b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         450      1446734.0           12669    32433.20393285361   0.9467532324494885  0.8378571513042986  0.8360845068688169\n",
+      "agg_4813  classical monocyte  left lung                         lung            NA                                              ENCODE                                scimilarity  nan     nan                         16       40636.89964582212   7786     32627.503591080356  0.7956982592153874  0.7257891637293196  0.6945729831399207\n",
+      "agg_4814  classical monocyte  liver                             liver           Alagille syndrome                               GSE163650                             scimilarity  nan     nan                         92       188745.0            7678     24676.271768005652  0.8778490663838523  0.7727787177978541  0.7475297818459176\n",
+      "agg_4815  classical monocyte  liver                             liver           Biliary atresia                                 GSE163650                             scimilarity  nan     nan                         367      1615410.0           11423    31280.031237098276  0.9308940943983487  0.8275363405345841  0.8098394949074608\n",
+      "agg_4816  classical monocyte  liver                             liver           fibrosis                                        GSE136103                             scimilarity  nan     nan                         1053     5824229.0           14559    31950.801484792315  0.9632028296773183  0.8486875622591808  0.8450295232730788\n",
+      "agg_4817  classical monocyte  liver                             liver           healthy                                         GSE136103                             scimilarity  nan     nan                         2036     7668787.0           14818    32446.71686472893   0.9614468191909097  0.8455504517344443  0.8464065028454753\n",
+      "agg_4818  classical monocyte  liver                             liver           healthy                                         GSE159977                             scimilarity  nan     nan                         584      4703990.0           13644    34306.87421529999   0.9580119653448788  0.8463006395755477  0.8456137609888349\n",
+      "agg_4819  classical monocyte  liver                             liver           healthy                                         GSE163650                             scimilarity  nan     nan                         440      4840312.0           12272    31180.407161439263  0.9312379663603724  0.8198899526213368  0.8071334502842269\n",
+      "agg_4820  classical monocyte  liver                             liver           non-alcoholic fatty liver disease               GSE136103                             scimilarity  nan     nan                         675      3625081.0           13858    31875.772311414476  0.9607644852684971  0.8451153856892568  0.8434410362324897\n",
+      "agg_4821  classical monocyte  liver                             liver           non-alcoholic steatohepatitis                   GSE159977                             scimilarity  nan     nan                         818      5328417.0           13712    34244.79288663241   0.9625204736588413  0.8495046961098656  0.843851264103893\n",
+      "agg_4822  classical monocyte  lower lobe of left lung           lung            NA                                              ENCODE                                scimilarity  nan     nan                         119      332235.9213328175   13992    45607.18635453728   0.9075609223976155  0.8257092626936027  0.8254709531577699\n",
+      "agg_4823  classical monocyte  lower lobe of lung                lung            healthy                                         GSE169471                             scimilarity  nan     nan                         305      1224338.0           11342    28255.985922767635  0.9404343350984603  0.8261785132237449  0.8150341534919611\n",
+      "agg_4824  classical monocyte  lung                              lung            COVID-19                                        GSE145926                             scimilarity  nan     nan                         6755     29326462.0          15670    32143.238185602037  0.9347325303698273  0.8382075353537076  0.8350131792189084\n",
+      "agg_4825  classical monocyte  lung                              lung            COVID-19                                        GSE149878                             scimilarity  nan     nan                         1388     17477477.0          15453    32118.37391645824   0.9547396360778304  0.8488321261303191  0.8334226404565429\n",
+      "agg_4826  classical monocyte  lung                              lung            COVID-19                                        covid                                 scimilarity  nan     nan                         87       182436.0            8979     30922.571470240666  0.8944321462094855  0.7906545756360502  0.7941388516891991\n",
+      "agg_4827  classical monocyte  lung                              lung            Idiopathic pulmonary arterial hypertension      GSE169471                             scimilarity  nan     nan                         338      1099281.0           11441    29048.65706098467   0.9394268881459132  0.8205211245968264  0.8008915371926529\n",
+      "agg_4828  classical monocyte  lung                              lung            NA                                              GSE122960                             scimilarity  nan     nan                         2035     4747594.0           13592    31170.75054081323   0.947029489389696   0.8239671657460403  0.8229589179837169\n",
+      "agg_4829  classical monocyte  lung                              lung            NA                                              GSE150708                             scimilarity  nan     nan                         1711     18922764.0          15768    34651.58457127426   0.9197817700449655  0.8258478966731367  0.8332402246613281\n",
+      "agg_4830  classical monocyte  lung                              lung            NA                                              GSE159354                             scimilarity  nan     nan                         804      1319717.0           12267    30466.67987009986   0.9289888078126158  0.8179900347657229  0.8046761734394422\n",
+      "agg_4831  classical monocyte  lung                              lung            chronic obstructive pulmonary disease           DS000011735                           scimilarity  nan     nan                         1757     5736362.0           16385    37750.32029026267   0.8922650938609307  0.8174310943962452  0.7970204631208431\n",
+      "agg_4832  classical monocyte  lung                              lung            healthy                                         5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         1254     5397217.0           13298    31013.01576531177   0.9490555294982109  0.8457060773457411  0.8329330004620054\n",
+      "agg_4833  classical monocyte  lung                              lung            healthy                                         DS000011735                           scimilarity  nan     nan                         4653     16523051.0          17066    37593.98867222708   0.8985708908575675  0.8260014412964971  0.8051199142820423\n",
+      "agg_4834  classical monocyte  lung                              lung            healthy                                         GSE128033                             scimilarity  nan     nan                         1047     3646581.0           13185    29331.2035341724    0.9513045959002788  0.837770557527153   0.8238987539695043\n",
+      "agg_4835  classical monocyte  lung                              lung            healthy                                         GSE128169                             scimilarity  nan     nan                         1732     11798577.0          15051    32941.755873862814  0.9636814596401634  0.8539834251349044  0.8457626672394015\n",
+      "agg_4836  classical monocyte  lung                              lung            healthy                                         GSE132771                             scimilarity  nan     nan                         1601     4614408.0           13275    29761.03818692572   0.9531291409651284  0.8457644131717956  0.8310980214000753\n",
+      "agg_4837  classical monocyte  lung                              lung            healthy                                         GSE169471                             scimilarity  nan     nan                         498      1613976.0           11886    28956.213107123967  0.9433400854993297  0.8271067022019086  0.815595680192867\n",
+      "agg_4838  classical monocyte  lung                              lung            hypersensitivity pneumonitis                    GSE122960                             scimilarity  nan     nan                         374      1513589.0           11850    30594.494180377842  0.9436379625667726  0.8236248274374875  0.8201281668004226\n",
+      "agg_4839  classical monocyte  lung                              lung            idiopathic pulmonary fibrosis                   DS000011735                           scimilarity  nan     nan                         3273     11098539.0          16692    36983.80245044498   0.9002060376489591  0.825628591643822   0.8057309447193657\n",
+      "agg_4840  classical monocyte  lung                              lung            idiopathic pulmonary fibrosis                   GSE122960                             scimilarity  nan     nan                         795      2302741.0           12763    31758.949309599942  0.9481965424789537  0.8315974088368188  0.8291678346888611\n",
+      "agg_4841  classical monocyte  lung                              lung            idiopathic pulmonary fibrosis                   GSE128033                             scimilarity  nan     nan                         264      892053.0            10997    28549.410927787198  0.9388857876621541  0.8259212982973368  0.8088642639807162\n",
+      "agg_4842  classical monocyte  lung                              lung            idiopathic pulmonary fibrosis                   GSE132771                             scimilarity  nan     nan                         562      1301612.0           12354    30446.385456748263  0.9469963680992495  0.8353213795556867  0.820474733820443\n",
+      "agg_4844  classical monocyte  lung                              lung            idiopathic pulmonary fibrosis                   GSE143706                             scimilarity  nan     nan                         28       77859.0             5999     21933.076720558005  0.8162248508692449  0.6872290766118224  0.6675933221673609\n",
+      "agg_4845  classical monocyte  lung                              lung            idiopathic pulmonary fibrosis                   GSE146981                             scimilarity  nan     nan                         28       77859.0             5999     21933.076720558005  0.8151661654118738  0.6848887765520302  0.667713152235204\n",
+      "agg_4846  classical monocyte  lung                              lung            idiopathic pulmonary fibrosis                   GSE159354                             scimilarity  nan     nan                         963      1825354.0           12518    29731.366588446697  0.9431677835768482  0.8335606700375219  0.8147956336542009\n",
+      "agg_4847  classical monocyte  lung                              lung            interstitial lung disease                       GSE122960                             scimilarity  nan     nan                         255      622277.0            10480    29149.028142322823  0.9283467350849584  0.8054016349869099  0.8007862785744999\n",
+      "agg_4848  classical monocyte  lung                              lung            interstitial lung disease                       GSE128169                             scimilarity  nan     nan                         697      1972432.0           12243    29254.839846468705  0.9423878335093786  0.8300974626358707  0.8196994274197087\n",
+      "agg_4849  classical monocyte  lung                              lung            scleroderma                                     GSE128169                             scimilarity  nan     nan                         108      906362.0            11850    32908.696557354284  0.9438117150692724  0.8371885386238901  0.8225520820106291\n",
+      "agg_4850  classical monocyte  lung                              lung            scleroderma                                     GSE132771                             scimilarity  nan     nan                         98       335776.0            9515     28212.056049440183  0.9149834322044056  0.8124364794406282  0.7889831369493125\n",
+      "agg_4851  classical monocyte  lung                              lung            systemic scleroderma;interstitial lung disease  GSE159354                             scimilarity  nan     nan                         680      1244200.0           11364    27669.832360293723  0.9311681193836778  0.8218066897336025  0.801632654842017\n",
+      "agg_4852  classical monocyte  lung parenchyma                   lung            COVID-19                                        GSE158127                             scimilarity  nan     nan                         1028     2949423.0           13468    33486.58561312063   0.9573331305866764  0.8476148647425674  0.8402420746316328\n",
+      "agg_4853  classical monocyte  lung parenchyma                   lung            healthy                                         GSE158127                             scimilarity  nan     nan                         791      2735456.0           13260    33646.87553319058   0.9544351950657847  0.8399981618864122  0.8327646420552953\n",
+      "agg_4854  classical monocyte  lymph node                        lymph node      Langerhans Cell Histiocytosis                   GSE133704                             scimilarity  nan     nan                         41       112531.0            7250     25404.282603262254  0.8424914182716917  0.7490029862443883  0.7315420690462492\n",
+      "agg_4855  classical monocyte  mesenteric artery                 vasculature     healthy                                         GSE156341                             scimilarity  nan     nan                         49       408553.0            10083    30979.99239432764   0.9337200372314851  0.8169964046619824  0.8163109060481625\n",
+      "agg_4856  classical monocyte  mesenteric artery                 vasculature     type II diabetes mellitus                       GSE156341                             scimilarity  nan     nan                         107      869426.0            11343    33124.102127533     0.9473048055491107  0.8341783608252021  0.8308049817701406\n",
+      "agg_4857  classical monocyte  mesenteric lymph node             lymph node      healthy                                         7681c7d7-0168-4892-a547-6f02a6430ace  scimilarity  nan     nan                         23       211416.0            9219     31018.02041830142   0.9058403141644794  0.7914556298280883  0.7867496166249129\n",
+      "agg_4858  classical monocyte  muscle tissue                     muscle          healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         1800     50754027.0          16141    33323.26969602708   0.9316672745867468  0.8348295016474899  0.8219918807835366\n",
+      "agg_4859  classical monocyte  nasal cavity                      airway          COVID-19                                        03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         2907     12130592.0          15268    34541.15937505348   0.9398365208964624  0.8399826209015534  0.8406896100498221\n",
+      "agg_4860  classical monocyte  nasal cavity                      airway          healthy                                         03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         129      319231.0            10961    33948.695520725145  0.908031594986554   0.802885512146251   0.7998873890770253\n",
+      "agg_4861  classical monocyte  nasopharynx                       airway          nasopharyngeal neoplasm                         GSE150825                             scimilarity  nan     nan                         248      919710.0            11652    32725.826204558824  0.9483756316738621  0.8416477499125612  0.8434913191320811\n",
+      "agg_4862  classical monocyte  nose                              airway          chronic rhinosinusitis with nasal polyps        GSE156285                             scimilarity  nan     nan                         89       407982.0            10658    32874.53627471163   0.9356986809715955  0.8282644612568757  0.8193450383260489\n",
+      "agg_4863  classical monocyte  olfactory epithelium              airway          NA                                              GSE139522                             scimilarity  nan     nan                         152      645745.0            11496    32760.770927681508  0.9344670047519139  0.8326335387583638  0.8215291009295241\n",
+      "agg_4864  classical monocyte  omental fat pad                   peritoneum      obesity                                         GSE163830                             scimilarity  nan     nan                         248      603440.0            11376    33014.67391159742   0.9276969765366693  0.8135481953870003  0.8105166731324462\n",
+      "agg_4865  classical monocyte  omentum                           peritoneum      NA                                              GSE151889                             scimilarity  nan     nan                         106      233037.0            9833     30451.216970905818  0.9023265636700606  0.794410906944691   0.7787417127899898\n",
+      "agg_4868  classical monocyte  peritoneum                        peritoneum      NA                                              GSE130888                             scimilarity  nan     nan                         20547    75515682.0          16606    31611.601467579523  0.9640573553126023  0.8513803623213743  0.8467189186177884\n",
+      "agg_4869  classical monocyte  peritoneum                        peritoneum      healthy                                         GSE130888                             scimilarity  nan     nan                         297      509237.0            11456    32213.169493243313  0.9218188146119148  0.8055575625334659  0.8039145027939639\n",
+      "agg_4870  classical monocyte  prostate gland                    prostate        healthy                                         GSE145843                             scimilarity  nan     nan                         24       87555.0             5997     21432.99958526179   0.816337811891654   0.7246769991610698  0.6943045232082502\n",
+      "agg_4871  classical monocyte  prostate gland                    prostate        healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         220      2445216.0           12460    33911.259081133416  0.9301914834403244  0.8259560877683964  0.824440772955729\n",
+      "agg_4872  classical monocyte  renal medulla                     kidney          healthy                                         120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         21       101089.0            7354     26191.96315382653   0.8491245141516279  0.751292403843078   0.7310820608427115\n",
+      "agg_4873  classical monocyte  respiratory airway                airway          COVID-19                                        29f92179-ca10-4309-a32b-d383d80347c1  scimilarity  nan     nan                         24222    187246624.0         17810    38621.12130270373   0.911673853186318   0.8025805020768422  0.8054824859649656\n",
+      "agg_4874  classical monocyte  respiratory tract epithelium      airway          NA                                              GSE139522                             scimilarity  nan     nan                         69       371203.0            11152    33714.18718588416   0.9189481336748234  0.8044925205508522  0.8036843863420214\n",
+      "agg_4875  classical monocyte  right cardiac atrium              heart           healthy                                         b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         311      977934.0            11891    31570.89048372305   0.9357294628028995  0.8260631626226992  0.8224053236502149\n",
+      "agg_4876  classical monocyte  sigmoid colon                     gut             ulcerative colitis                              DS000010618                           scimilarity  nan     nan                         56       157830.0            7772     25795.237651990203  0.8795503731261843  0.7579451181338609  0.7495331251552053\n",
+      "agg_4877  classical monocyte  spleen                            spleen          HIV infection                                   GSE148796                             scimilarity  nan     nan                         48       118392.0            7120     25206.082214526155  0.8589535988735265  0.7723626781751426  0.7544543625246387\n",
+      "agg_4878  classical monocyte  spleen                            spleen          healthy                                         4d74781b-8186-4c9a-b659-ff4dc4601d91  scimilarity  nan     nan                         2166     7905128.0           13952    30832.98149016589   0.957626764427953   0.8498277489734691  0.8368540073560422\n",
+      "agg_4879  classical monocyte  spleen                            spleen          healthy                                         GSE148796                             scimilarity  nan     nan                         49       99684.0             6785     24492.260723886982  0.8477188947186437  0.7504685014947085  0.7295165069095441\n",
+      "agg_4880  classical monocyte  spleen                            spleen          healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         3483     86078540.0          17775    36266.36759486      0.9100465322095738  0.8097019968114763  0.8081945289287479\n",
+      "agg_4881  classical monocyte  subcutaneous adipose tissue       adipose         healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         2019     27914261.0          15739    35142.992419516115  0.9421666034710219  0.8357406603138717  0.8249091266671545\n",
+      "agg_4882  classical monocyte  synovial fluid                    synovial joint  juvenile idiopathic arthritis                   GSE160097                             scimilarity  nan     nan                         68       430194.0            9771     30748.449183442222  0.9095023395695137  0.8036624580038914  0.8039477211085259\n",
+      "agg_4883  classical monocyte  synovial fluid                    synovial joint  psoriatic arthritis                             GSE161500                             scimilarity  nan     nan                         675      4107697.0           12980    33508.9642503863    0.9516464026609945  0.8477381221507095  0.8460202124356817\n",
+      "agg_4884  classical monocyte  tertiary ovarian follicle         ovary           NA                                              GSE146512                             scimilarity  nan     nan                         100      296748.0            10411    33084.00694991347   0.9139378500397654  0.8064064097295065  0.8023402394768804\n",
+      "agg_4885  classical monocyte  testis                            testis          NA                                              GSE153819                             scimilarity  nan     nan                         17       97625.0             7958     29072.297180853668  0.855345538109863   0.764463114789657   0.7520891122089112\n",
+      "agg_4886  classical monocyte  thoracic lymph node               lymph node      healthy                                         62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         20194    147502356.0         17156    34278.43204322054   0.9584542376154844  0.8568164088302802  0.8468420651789338\n",
+      "agg_4887  classical monocyte  thymus                            thymus          healthy                                         62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         487      2692133.0           12969    33549.45213075861   0.9497232200872254  0.8549259958290932  0.843613914494647\n",
+      "agg_4888  classical monocyte  thymus                            thymus          healthy                                         83ed3be8-4cb9-43e6-9aaa-3fbbf5d1bd3a  scimilarity  nan     nan                         27       80042.0             6527     23800.833698344715  0.8448423801602987  0.7347769941739797  0.7181319535525903\n",
+      "agg_4889  classical monocyte  thymus                            thymus          healthy                                         de13e3e2-23b6-40ed-a413-e9e12d7d3910  scimilarity  nan     nan                         52       298983.0            9582     30002.684399867492  0.9175531872094376  0.8185046437128072  0.8176606503789746\n",
+      "agg_4890  classical monocyte  tonsil                            tonsil          healthy                                         GSE119506                             scimilarity  nan     nan                         321      1114339.0           11546    30516.473940957327  0.936749794981687   0.8307356996245999  0.8215841775914924\n",
+      "agg_4893  classical monocyte  trachea                           airway          healthy                                         03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         126      457580.0            11135    33734.327765812595  0.9299687996532033  0.8279876048002456  0.8230768581762299\n",
+      "agg_4894  classical monocyte  trachea                           airway          healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         130      8245622.0           13663    32584.555010387805  0.859406194907081   0.7522520630806427  0.7550758397192211\n",
+      "agg_4895  classical monocyte  transition zone of prostate       prostate        prostatic hypertrophy                           4b54248f-2165-477c-a027-dd55082e8818  scimilarity  nan     nan                         520      2949099.0           13618    29095.077520604846  0.9205242051339535  0.807648434373933   0.7862293702463532\n",
+      "agg_4896  classical monocyte  transverse colon                  gut             healthy                                         62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         503      2932999.0           12868    33265.22762476361   0.9476555210791636  0.8481166846094836  0.8392740144102075\n",
+      "agg_4897  classical monocyte  tympanic membrane                 ear             NA                                              GSE128892                             scimilarity  nan     nan                         33       153723.0            7971     26217.848855873086  0.8515891731868822  0.7526405790618411  0.7328434865867175\n",
+      "agg_4899  classical monocyte  upper lobe of lung                lung            healthy                                         GSE169471                             scimilarity  nan     nan                         180      594059.0            10222    27881.011904781462  0.9248153838159024  0.8106326147324702  0.8003438801026445\n",
+      "agg_4900  classical monocyte  urine                             urinary         healthy                                         GSE165396                             scimilarity  nan     nan                         20       109197.0            7299     25505.26771828214   0.8530560359297166  0.7505269381795711  0.7350625083451162\n",
+      "agg_4901  classical monocyte  uterus                            uterus          healthy                                         32f2fd23-ec74-486f-9544-e5b2f41725f5  scimilarity  nan     nan                         18       189472.0            9397     30891.07905591756   0.8870309576225303  0.7796319609750195  0.7827462282311286\n",
+      "agg_4902  classical monocyte  vasculature                       vasculature     healthy                                         e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         14537    224206261.0         17922    36651.90273209977   0.9438232475974557  0.8425086108952623  0.8343079266995497\n",
+      "agg_4903  classical monocyte  visceral fat                      adipose         obesity                                         GSE128518                             scimilarity  nan     nan                         74       196657.0            9100     28836.082756573305  0.8890453654508667  0.7810993233599736  0.7788838086708725\n"
      ]
     },
     {
@@ -1393,31 +1831,161 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:29:20.091032Z",
-     "iopub.status.busy": "2025-11-21T06:29:20.090868Z",
-     "iopub.status.idle": "2025-11-21T06:29:35.058170Z",
-     "shell.execute_reply": "2025-11-21T06:29:35.057422Z"
-    }
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\r\n"
+      "          cell_type               tissue                            organ           disease  study                                 dataset      region  subregion  celltype_coarse  n_cells  total_counts  n_genes  size_factor         train_pearson       val_pearson         test_pearson\n",
+      "agg_4706  classical monocyte      alveolar system                   lung            healthy  GSE155249                             scimilarity  nan     nan                         72       218105.0      9142     30484.31888978114   0.9102228263646758  0.8083487523192785  0.8047828694155461\n",
+      "agg_4707  classical monocyte      ampulla of uterine tube           fallopian tube  healthy  fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         78       550950.0      9639     30719.377971431015  0.9077670011915634  0.8045070167513724  0.7896845423359651\n",
+      "agg_4709  classical monocyte      aorta                             vasculature     healthy  GSE166676                             scimilarity  nan     nan                         25       162858.0      8859     31216.275954364824  0.8819013257206973  0.7821403055329706  0.7646999711802146\n",
+      "agg_4710  classical monocyte      apex of heart                     heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         397      1226515.0     12369    32022.563851814968  0.9469178617442242  0.8326145310572417  0.8365506153530168\n",
+      "agg_4733  classical monocyte      blood                             blood           healthy  03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         32464    109280914.0   16158    33646.02843110038   0.9568728712803031  0.8545324533535094  0.8487445540580735\n",
+      "agg_4734  classical monocyte      blood                             blood           healthy  436154da-bcf1-4130-9c8b-120ff9a888f2  scimilarity  nan     nan                         76800    206490628.0   16683    30736.453324546856  0.955313650235467   0.8494127267867799  0.84210567312908\n",
+      "agg_4735  classical monocyte      blood                             blood           healthy  5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         1044     3638976.0     12306    30542.8977364245    0.9465384578921433  0.851991795617166   0.8308191715256182\n",
+      "agg_4736  classical monocyte      blood                             blood           healthy  DS000010023                           scimilarity  nan     nan                         243      362606.0      8414     25201.261024165953  0.8865446516098515  0.7621391376670988  0.7681818769796727\n",
+      "agg_4737  classical monocyte      blood                             blood           healthy  GSE122703                             scimilarity  nan     nan                         18       83417.0       7546     28194.725173315186  0.859225612396475   0.7640890699253299  0.7443681539461423\n",
+      "agg_4738  classical monocyte      blood                             blood           healthy  GSE130117                             scimilarity  nan     nan                         2017     7130588.0     13535    33078.53692191542   0.9553673450160365  0.851402109626239   0.8385936100758409\n",
+      "agg_4739  classical monocyte      blood                             blood           healthy  GSE132802                             scimilarity  nan     nan                         1601     9955248.0     13132    32063.630951743195  0.9478882791739611  0.8391025143866828  0.8303465877530952\n",
+      "agg_4740  classical monocyte      blood                             blood           healthy  GSE139324                             scimilarity  nan     nan                         2333     8331045.0     13985    31135.881287246768  0.9608208780142045  0.8473885992448625  0.8432790193723467\n",
+      "agg_4741  classical monocyte      blood                             blood           healthy  GSE145809                             scimilarity  nan     nan                         69       245221.0      8962     29135.67197629852   0.8825701041728526  0.7811799267734735  0.7818647625179129\n",
+      "agg_4742  classical monocyte      blood                             blood           healthy  GSE149313                             scimilarity  nan     nan                         2420     6974751.0     13143    29560.496854576566  0.9574598613513423  0.8505290963248237  0.8379199735887167\n",
+      "agg_4743  classical monocyte      blood                             blood           healthy  GSE153421                             scimilarity  nan     nan                         3691     15561725.0    14569    34377.465875728165  0.9636686704566925  0.8576434473562725  0.8511814190737197\n",
+      "agg_4744  classical monocyte      blood                             blood           healthy  GSE156989                             scimilarity  nan     nan                         13554    160011485.0   16915    34135.439844737564  0.9640667421350761  0.8577967800377495  0.8517975138366085\n",
+      "agg_4745  classical monocyte      blood                             blood           healthy  GSE157829                             scimilarity  nan     nan                         1619     6957811.0     13507    30199.39288988673   0.9484019976492215  0.8436979316400604  0.8347196616710685\n",
+      "agg_4746  classical monocyte      blood                             blood           healthy  GSE159113                             scimilarity  nan     nan                         1025     6298250.0     12083    27477.50809897617   0.9078020151513733  0.8121457150205226  0.7980372877810575\n",
+      "agg_4747  classical monocyte      blood                             blood           healthy  GSE161329                             scimilarity  nan     nan                         5654     25653579.0    14349    28848.0539929647    0.9549801428956252  0.8450430950674043  0.8406188789518544\n",
+      "agg_4748  classical monocyte      blood                             blood           healthy  GSE161738                             scimilarity  nan     nan                         2676     13801473.0    12825    33337.477050230416  0.9541962906717452  0.8512846409758499  0.8485408028961247\n",
+      "agg_4749  classical monocyte      blood                             blood           healthy  GSE163668                             scimilarity  nan     nan                         2644     10486314.0    14049    33786.96584264489   0.9597801578342394  0.8560775485935677  0.8512149509551471\n",
+      "agg_4750  classical monocyte      blood                             blood           healthy  GSE166992                             scimilarity  nan     nan                         7501     28033216.0    15079    33455.367364577316  0.9622273594219685  0.8558958139235102  0.8495571689751152\n",
+      "agg_4751  classical monocyte      blood                             blood           healthy  GSE167363                             scimilarity  nan     nan                         3135     14722635.0    14375    29977.24002819913   0.942417448875388   0.8368071803109702  0.8258536430202982\n",
+      "agg_4752  classical monocyte      blood                             blood           healthy  GSE168710                             scimilarity  nan     nan                         16484    104881872.0   16223    34107.336261357574  0.9398282119039322  0.8424821834537695  0.8372971004604842\n",
+      "agg_4753  classical monocyte      blood                             blood           healthy  GSE168732                             scimilarity  nan     nan                         770      2548822.0     12508    33411.30103713399   0.9552513581030765  0.8508279875038706  0.847461536110767\n",
+      "agg_4754  classical monocyte      blood                             blood           healthy  b0cf0afa-ec40-4d65-b570-ed4ceacc6813  scimilarity  nan     nan                         40975    300555227.0   15784    35938.85772500803   0.9622425892039956  0.853424173800979   0.8508714303589978\n",
+      "agg_4755  classical monocyte      blood                             blood           healthy  ddfad306-714d-4cc0-9985-d9072820c530  scimilarity  nan     nan                         8827     36073928.0    15131    33208.591584008376  0.9546118779961532  0.8543086616569785  0.8462739374830107\n",
+      "agg_4772  classical monocyte      bone marrow                       bone marrow     healthy  GSE132509                             scimilarity  nan     nan                         610      2315570.0     12950    31768.06513427212   0.95159369508558    0.8517118261701931  0.836658919433696\n",
+      "agg_4773  classical monocyte      bone marrow                       bone marrow     healthy  GSE154109                             scimilarity  nan     nan                         531      1431388.0     11793    31377.450948003392  0.9490546933955852  0.8431566630120637  0.8370883160295727\n",
+      "agg_4774  classical monocyte      bone marrow                       bone marrow     healthy  GSE163278                             scimilarity  nan     nan                         1119     3970394.0     13361    32081.93302956569   0.9620394897163868  0.8531148861215617  0.8426785397396367\n",
+      "agg_4775  classical monocyte      bone marrow                       bone marrow     healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         151      8025584.0     12883    29444.78352863075   0.8440608178227607  0.740574910750325   0.7417328577454956\n",
+      "agg_4777  classical monocyte      breast                            breast          healthy  GSE164898                             scimilarity  nan     nan                         136      641471.0      12971    34463.52724138501   0.9163324788498406  0.8116274576633968  0.7978555908123931\n",
+      "agg_4778  classical monocyte      breast                            breast          healthy  c9706a92-0e5f-46c1-96d8-20e42467f287  scimilarity  nan     nan                         98       1444245.0     13491    30678.263421880285  0.9165520953567395  0.8162053142576849  0.7994301225229256\n",
+      "agg_4782  classical monocyte      bronchus                          airway          healthy  GSE158127                             scimilarity  nan     nan                         158      1158198.0     12643    34764.50196701077   0.9364512338084163  0.8259291909369686  0.8266638555276521\n",
+      "agg_4783  classical monocyte      cardiac muscle of left ventricle  heart           healthy  GSE156703                             scimilarity  nan     nan                         13       116181.0      9463     35695.66320276271   0.8542740960069863  0.7515621053395214  0.7561639038477878\n",
+      "agg_4785  classical monocyte      caudate lobe of liver             liver           healthy  44531dd9-1388-4416-a117-af0a99de2294  scimilarity  nan     nan                         238      730016.0      11505    31342.386314731422  0.9217674983890346  0.8140551552218395  0.8040417787954989\n",
+      "agg_4786  classical monocyte      cortex of kidney                  kidney          healthy  120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         79       323010.0      10939    32378.76683324232   0.9028856137251035  0.7978822439778066  0.7839454035009307\n",
+      "agg_4787  classical monocyte      cortex of kidney                  kidney          healthy  a98b828a-622a-483a-80e0-15703678befd  scimilarity  nan     nan                         91       477355.0      10898    32358.068865763344  0.9328436291917394  0.8237810319569842  0.8195391931798526\n",
+      "agg_4789  classical monocyte      digestive tract                   gut             healthy  DS000011665                           scimilarity  nan     nan                         347      1679116.0     12155    33347.55517047197   0.9422556928648441  0.8417267634096297  0.84018452536733\n",
+      "agg_4790  classical monocyte      exocrine pancreas                 pancreas        healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         821      7824069.0     14847    36135.64587109593   0.9493709998172055  0.8440837716099078  0.8410246939313819\n",
+      "agg_4791  classical monocyte      fallopian tube                    fallopian tube  healthy  fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         131      734093.0      11240    33115.640434504094  0.9359103734457376  0.8339026306142181  0.8225901799509813\n",
+      "agg_4792  classical monocyte      fimbria of uterine tube           fallopian tube  healthy  fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         34       209362.0      7663     27635.733860508328  0.8560416684135254  0.7382997749370328  0.7459235366949488\n",
+      "agg_4795  classical monocyte      head of femur                     bone            healthy  GSE169396                             scimilarity  nan     nan                         450      3669304.0     13216    33082.323604222154  0.9529417082022753  0.8522346343107771  0.8359032081996703\n",
+      "agg_4798  classical monocyte      heart left ventricle              heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         192      585001.0      11159    31422.874036870588  0.9363985001217598  0.8226438123601741  0.8283173244446851\n",
+      "agg_4799  classical monocyte      heart right ventricle             heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         316      936263.0      11904    32002.67227624691   0.9425900348990802  0.8306128730459813  0.8308116461021977\n",
+      "agg_4803  classical monocyte      interventricular septum           heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         442      1322725.0     12418    32235.5434681197    0.94751399102473    0.8340623939411483  0.8353858365852226\n",
+      "agg_4804  classical monocyte      isthmus of fallopian tube         fallopian tube  healthy  fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         62       318330.0      8642     29768.198512126502  0.8846027131590791  0.784220371073871   0.7739277341448829\n",
+      "agg_4808  classical monocyte      kidney                            kidney          healthy  120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         762      4034828.0     14946    34015.00816823295   0.9520640694425091  0.848205266473767   0.836085027208869\n",
+      "agg_4809  classical monocyte      kidney                            kidney          healthy  DS000010415                           scimilarity  nan     nan                         55       127079.0      8055     27206.419037355434  0.8216238135756493  0.7570847030479543  0.72524174726152\n",
+      "agg_4810  classical monocyte      kidney                            kidney          healthy  GSE140989                             scimilarity  nan     nan                         174      563438.0      11016    29887.593299155575  0.914390252807459   0.8069762795735104  0.8086759072557722\n",
+      "agg_4812  classical monocyte      left cardiac atrium               heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         450      1446734.0     12669    32433.20393285361   0.9467532324494885  0.8378571513042986  0.8360845068688169\n",
+      "agg_4817  classical monocyte      liver                             liver           healthy  GSE136103                             scimilarity  nan     nan                         2036     7668787.0     14818    32446.71686472893   0.9614468191909097  0.8455504517344443  0.8464065028454753\n",
+      "agg_4818  classical monocyte      liver                             liver           healthy  GSE159977                             scimilarity  nan     nan                         584      4703990.0     13644    34306.87421529999   0.9580119653448788  0.8463006395755477  0.8456137609888349\n",
+      "agg_4819  classical monocyte      liver                             liver           healthy  GSE163650                             scimilarity  nan     nan                         440      4840312.0     12272    31180.407161439263  0.9312379663603724  0.8198899526213368  0.8071334502842269\n",
+      "agg_4823  classical monocyte      lower lobe of lung                lung            healthy  GSE169471                             scimilarity  nan     nan                         305      1224338.0     11342    28255.985922767635  0.9404343350984603  0.8261785132237449  0.8150341534919611\n",
+      "agg_4832  classical monocyte      lung                              lung            healthy  5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         1254     5397217.0     13298    31013.01576531177   0.9490555294982109  0.8457060773457411  0.8329330004620054\n",
+      "agg_4833  classical monocyte      lung                              lung            healthy  DS000011735                           scimilarity  nan     nan                         4653     16523051.0    17066    37593.98867222708   0.8985708908575675  0.8260014412964971  0.8051199142820423\n",
+      "agg_4834  classical monocyte      lung                              lung            healthy  GSE128033                             scimilarity  nan     nan                         1047     3646581.0     13185    29331.2035341724    0.9513045959002788  0.837770557527153   0.8238987539695043\n",
+      "agg_4835  classical monocyte      lung                              lung            healthy  GSE128169                             scimilarity  nan     nan                         1732     11798577.0    15051    32941.755873862814  0.9636814596401634  0.8539834251349044  0.8457626672394015\n",
+      "agg_4836  classical monocyte      lung                              lung            healthy  GSE132771                             scimilarity  nan     nan                         1601     4614408.0     13275    29761.03818692572   0.9531291409651284  0.8457644131717956  0.8310980214000753\n",
+      "agg_4837  classical monocyte      lung                              lung            healthy  GSE169471                             scimilarity  nan     nan                         498      1613976.0     11886    28956.213107123967  0.9433400854993297  0.8271067022019086  0.815595680192867\n",
+      "agg_4853  classical monocyte      lung parenchyma                   lung            healthy  GSE158127                             scimilarity  nan     nan                         791      2735456.0     13260    33646.87553319058   0.9544351950657847  0.8399981618864122  0.8327646420552953\n",
+      "agg_4855  classical monocyte      mesenteric artery                 vasculature     healthy  GSE156341                             scimilarity  nan     nan                         49       408553.0      10083    30979.99239432764   0.9337200372314851  0.8169964046619824  0.8163109060481625\n",
+      "agg_4857  classical monocyte      mesenteric lymph node             lymph node      healthy  7681c7d7-0168-4892-a547-6f02a6430ace  scimilarity  nan     nan                         23       211416.0      9219     31018.02041830142   0.9058403141644794  0.7914556298280883  0.7867496166249129\n",
+      "agg_4858  classical monocyte      muscle tissue                     muscle          healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         1800     50754027.0    16141    33323.26969602708   0.9316672745867468  0.8348295016474899  0.8219918807835366\n",
+      "agg_4860  classical monocyte      nasal cavity                      airway          healthy  03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         129      319231.0      10961    33948.695520725145  0.908031594986554   0.802885512146251   0.7998873890770253\n",
+      "agg_4869  classical monocyte      peritoneum                        peritoneum      healthy  GSE130888                             scimilarity  nan     nan                         297      509237.0      11456    32213.169493243313  0.9218188146119148  0.8055575625334659  0.8039145027939639\n",
+      "agg_4870  classical monocyte      prostate gland                    prostate        healthy  GSE145843                             scimilarity  nan     nan                         24       87555.0       5997     21432.99958526179   0.816337811891654   0.7246769991610698  0.6943045232082502\n",
+      "agg_4871  classical monocyte      prostate gland                    prostate        healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         220      2445216.0     12460    33911.259081133416  0.9301914834403244  0.8259560877683964  0.824440772955729\n",
+      "agg_4872  classical monocyte      renal medulla                     kidney          healthy  120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         21       101089.0      7354     26191.96315382653   0.8491245141516279  0.751292403843078   0.7310820608427115\n",
+      "agg_4875  classical monocyte      right cardiac atrium              heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         311      977934.0      11891    31570.89048372305   0.9357294628028995  0.8260631626226992  0.8224053236502149\n",
+      "agg_4878  classical monocyte      spleen                            spleen          healthy  4d74781b-8186-4c9a-b659-ff4dc4601d91  scimilarity  nan     nan                         2166     7905128.0     13952    30832.98149016589   0.957626764427953   0.8498277489734691  0.8368540073560422\n",
+      "agg_4879  classical monocyte      spleen                            spleen          healthy  GSE148796                             scimilarity  nan     nan                         49       99684.0       6785     24492.260723886982  0.8477188947186437  0.7504685014947085  0.7295165069095441\n",
+      "agg_4880  classical monocyte      spleen                            spleen          healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         3483     86078540.0    17775    36266.36759486      0.9100465322095738  0.8097019968114763  0.8081945289287479\n",
+      "agg_4881  classical monocyte      subcutaneous adipose tissue       adipose         healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         2019     27914261.0    15739    35142.992419516115  0.9421666034710219  0.8357406603138717  0.8249091266671545\n",
+      "agg_4886  classical monocyte      thoracic lymph node               lymph node      healthy  62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         20194    147502356.0   17156    34278.43204322054   0.9584542376154844  0.8568164088302802  0.8468420651789338\n",
+      "agg_4887  classical monocyte      thymus                            thymus          healthy  62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         487      2692133.0     12969    33549.45213075861   0.9497232200872254  0.8549259958290932  0.843613914494647\n",
+      "agg_4888  classical monocyte      thymus                            thymus          healthy  83ed3be8-4cb9-43e6-9aaa-3fbbf5d1bd3a  scimilarity  nan     nan                         27       80042.0       6527     23800.833698344715  0.8448423801602987  0.7347769941739797  0.7181319535525903\n",
+      "agg_4889  classical monocyte      thymus                            thymus          healthy  de13e3e2-23b6-40ed-a413-e9e12d7d3910  scimilarity  nan     nan                         52       298983.0      9582     30002.684399867492  0.9175531872094376  0.8185046437128072  0.8176606503789746\n",
+      "agg_4890  classical monocyte      tonsil                            tonsil          healthy  GSE119506                             scimilarity  nan     nan                         321      1114339.0     11546    30516.473940957327  0.936749794981687   0.8307356996245999  0.8215841775914924\n",
+      "agg_4893  classical monocyte      trachea                           airway          healthy  03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         126      457580.0      11135    33734.327765812595  0.9299687996532033  0.8279876048002456  0.8230768581762299\n",
+      "agg_4894  classical monocyte      trachea                           airway          healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         130      8245622.0     13663    32584.555010387805  0.859406194907081   0.7522520630806427  0.7550758397192211\n",
+      "agg_4896  classical monocyte      transverse colon                  gut             healthy  62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         503      2932999.0     12868    33265.22762476361   0.9476555210791636  0.8481166846094836  0.8392740144102075\n",
+      "agg_4899  classical monocyte      upper lobe of lung                lung            healthy  GSE169471                             scimilarity  nan     nan                         180      594059.0      10222    27881.011904781462  0.9248153838159024  0.8106326147324702  0.8003438801026445\n",
+      "agg_4900  classical monocyte      urine                             urinary         healthy  GSE165396                             scimilarity  nan     nan                         20       109197.0      7299     25505.26771828214   0.8530560359297166  0.7505269381795711  0.7350625083451162\n",
+      "agg_4901  classical monocyte      uterus                            uterus          healthy  32f2fd23-ec74-486f-9544-e5b2f41725f5  scimilarity  nan     nan                         18       189472.0      9397     30891.07905591756   0.8870309576225303  0.7796319609750195  0.7827462282311286\n",
+      "agg_4902  classical monocyte      vasculature                       vasculature     healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         14537    224206261.0   17922    36651.90273209977   0.9438232475974557  0.8425086108952623  0.8343079266995497\n",
+      "agg_6287  intermediate monocyte   head of femur                     bone            healthy  GSE169396                             scimilarity  nan     nan                         102      191075.0      7853     26179.035956297153  0.8330771518439726  0.7503209876663273  0.7113081302663875\n",
+      "agg_6289  intermediate monocyte   lung                              lung            healthy  5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         178      1172582.0     11314    30515.569680815937  0.9409051040470379  0.8435279441582394  0.8229749449946738\n",
+      "agg_6290  intermediate monocyte   spleen                            spleen          healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         60       252220.0      8236     26163.955486425446  0.836498746238646   0.7439980946911184  0.7168040239671246\n",
+      "agg_6291  intermediate monocyte   thymus                            thymus          healthy  GSE159745                             scimilarity  nan     nan                         29       82115.0       5987     22540.234815420707  0.7817665678679913  0.7043553158094606  0.6760717657015229\n",
+      "agg_6292  intermediate monocyte   vasculature                       vasculature     healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         162      1525515.0     11980    32186.1781582846    0.9269571292216415  0.8224395619954739  0.8055668264019443\n",
+      "agg_7919  non-classical monocyte  apex of heart                     heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         158      675731.0      11222    30876.899222310585  0.9301440448035162  0.8258961291954683  0.824159827752181\n",
+      "agg_7939  non-classical monocyte  blood                             blood           healthy  03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         7359     36418526.0    14851    33258.733090217436  0.9519276590597959  0.8501060197631037  0.850960653386464\n",
+      "agg_7940  non-classical monocyte  blood                             blood           healthy  436154da-bcf1-4130-9c8b-120ff9a888f2  scimilarity  nan     nan                         14619    54479703.0    15493    30191.499157063707  0.9490211472741342  0.8448211022633122  0.8418061408505518\n",
+      "agg_7941  non-classical monocyte  blood                             blood           healthy  5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         195      1239240.0     11002    30223.31815928311   0.9379359624101378  0.8397815574875395  0.8299059656859994\n",
+      "agg_7942  non-classical monocyte  blood                             blood           healthy  GSE130117                             scimilarity  nan     nan                         322      1200290.0     11182    31409.27333671809   0.9418345510352945  0.8405863822794143  0.8304108431330093\n",
+      "agg_7943  non-classical monocyte  blood                             blood           healthy  GSE132802                             scimilarity  nan     nan                         102      726593.0      10232    30435.311954214158  0.9262718659348109  0.8186907385315048  0.8147462064075649\n",
+      "agg_7944  non-classical monocyte  blood                             blood           healthy  GSE134004                             scimilarity  nan     nan                         21       123360.0      6990     24135.62187154626   0.8660944307658819  0.7608357820845066  0.7535994530765779\n",
+      "agg_7945  non-classical monocyte  blood                             blood           healthy  GSE139324                             scimilarity  nan     nan                         435      2395489.0     12428    30987.28210955269   0.9487843521961865  0.8347185640893492  0.8386196076774184\n",
+      "agg_7946  non-classical monocyte  blood                             blood           healthy  GSE149313                             scimilarity  nan     nan                         567      2445950.0     11737    29144.914765151065  0.9480753865144765  0.8398428245307203  0.8354975068540325\n",
+      "agg_7947  non-classical monocyte  blood                             blood           healthy  GSE153421                             scimilarity  nan     nan                         441      2114891.0     11966    32647.54233438482   0.9524227157990666  0.8453840087915955  0.8445318485082767\n",
+      "agg_7948  non-classical monocyte  blood                             blood           healthy  GSE156989                             scimilarity  nan     nan                         3151     40420221.0    15662    32233.769948686153  0.9558775883405131  0.8527879494870606  0.8461332941066178\n",
+      "agg_7949  non-classical monocyte  blood                             blood           healthy  GSE157829                             scimilarity  nan     nan                         144      890675.0      10657    29172.377435644317  0.9302786443476869  0.8283014100006509  0.8220722486967346\n",
+      "agg_7950  non-classical monocyte  blood                             blood           healthy  GSE161329                             scimilarity  nan     nan                         1118     7175719.0     12865    29244.857112487658  0.9476682128749678  0.8393859845018986  0.8392730294908749\n",
+      "agg_7951  non-classical monocyte  blood                             blood           healthy  GSE161738                             scimilarity  nan     nan                         1497     12143757.0    12362    32632.110821778042  0.9476207444575895  0.8463853459866758  0.8490136875251815\n",
+      "agg_7952  non-classical monocyte  blood                             blood           healthy  GSE163668                             scimilarity  nan     nan                         323      1716760.0     11769    32812.39505818172   0.9472316303194352  0.8438698542111769  0.8424970169398833\n",
+      "agg_7953  non-classical monocyte  blood                             blood           healthy  GSE166992                             scimilarity  nan     nan                         1613     7143035.0     13383    32605.389353410996  0.9532209129181096  0.8486599938147087  0.8451557766892072\n",
+      "agg_7954  non-classical monocyte  blood                             blood           healthy  GSE167363                             scimilarity  nan     nan                         458      3094035.0     12228    29678.03739631962   0.9435654504659038  0.8379948419319221  0.8241961683725367\n",
+      "agg_7955  non-classical monocyte  blood                             blood           healthy  GSE168710                             scimilarity  nan     nan                         75       701113.0      10776    32559.49721301115   0.9233871475920297  0.8250528447177075  0.8185333840139741\n",
+      "agg_7956  non-classical monocyte  blood                             blood           healthy  GSE168732                             scimilarity  nan     nan                         229      1242404.0     11269    31965.299177754878  0.9416742339845935  0.8377482237588458  0.8415500681607002\n",
+      "agg_7957  non-classical monocyte  blood                             blood           healthy  b0cf0afa-ec40-4d65-b570-ed4ceacc6813  scimilarity  nan     nan                         5897     43180935.0    14970    35595.18649267558   0.9543288699936718  0.8496535044071925  0.8536782531653531\n",
+      "agg_7970  non-classical monocyte  bone marrow                       bone marrow     healthy  GSE132509                             scimilarity  nan     nan                         28       86280.0       7085     25870.53624683356   0.8392355979135352  0.755387271124993   0.7303317003908475\n",
+      "agg_7971  non-classical monocyte  bone marrow                       bone marrow     healthy  GSE154109                             scimilarity  nan     nan                         50       289907.0      9147     28526.85661530531   0.9070952567396775  0.808361820498357   0.7964598438037417\n",
+      "agg_7972  non-classical monocyte  bone marrow                       bone marrow     healthy  GSE163278                             scimilarity  nan     nan                         127      682328.0      10897    31202.529821716787  0.9358619814006104  0.8294675777784289  0.8230468472234642\n",
+      "agg_7974  non-classical monocyte  breast                            breast          healthy  GSE164898                             scimilarity  nan     nan                         54       120275.0      7652     26423.010551181265  0.8534343500588628  0.755424451856796   0.7230125883881353\n",
+      "agg_7976  non-classical monocyte  cortex of kidney                  kidney          healthy  120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         63       401864.0      11464    32990.96311584531   0.9014527631813947  0.7931942866954178  0.7865930763896118\n",
+      "agg_7977  non-classical monocyte  cortex of kidney                  kidney          healthy  a98b828a-622a-483a-80e0-15703678befd  scimilarity  nan     nan                         161      772141.0      11062    31056.660350613587  0.9346806312815913  0.8317520409566359  0.8313984606487961\n",
+      "agg_7979  non-classical monocyte  fallopian tube                    fallopian tube  healthy  fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         16       222500.0      7755     27898.660349927846  0.8621134765305163  0.7601890611687369  0.744729424431635\n",
+      "agg_7980  non-classical monocyte  fimbria of uterine tube           fallopian tube  healthy  fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         28       244266.0      7735     27678.814270356754  0.8734809587140066  0.7648153554084077  0.76608428730196\n",
+      "agg_7982  non-classical monocyte  heart left ventricle              heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         57       213565.0      9322     29851.355460106315  0.8978296307325292  0.7939956162210045  0.776120799756924\n",
+      "agg_7983  non-classical monocyte  heart right ventricle             heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         124      613752.0      10880    30311.360727579897  0.9311389328985725  0.8253171421863371  0.8219320702682233\n",
+      "agg_7985  non-classical monocyte  interventricular septum           heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         144      524658.0      10853    31170.857886799495  0.9241302459076691  0.8138209035301205  0.8224136632163046\n",
+      "agg_7986  non-classical monocyte  isthmus of fallopian tube         fallopian tube  healthy  fc77d2ae-247d-44d7-aa24-3f4859254c2c  scimilarity  nan     nan                         12       86198.0       5668     23558.81110396413   0.793224614700874   0.7114672551137675  0.6825985710920001\n",
+      "agg_7990  non-classical monocyte  kidney                            kidney          healthy  120e86b4-1195-48c5-845b-b98054105eec  scimilarity  nan     nan                         214      1788808.0     13749    33794.71179753717   0.9324162382479619  0.8250902105825786  0.818301334304562\n",
+      "agg_7991  non-classical monocyte  kidney                            kidney          healthy  GSE140989                             scimilarity  nan     nan                         473      1769375.0     13008    31797.13462972748   0.9190676320157254  0.8182361375222441  0.8172110487458264\n",
+      "agg_7992  non-classical monocyte  left cardiac atrium               heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         82       357018.0      10073    29995.2881818953    0.916928150799629   0.8074046233049013  0.808753490409063\n",
+      "agg_7995  non-classical monocyte  liver                             liver           healthy  GSE136103                             scimilarity  nan     nan                         423      2383574.0     13122    31645.435149503457  0.9524231389227664  0.8376748793878739  0.8415142758287522\n",
+      "agg_7996  non-classical monocyte  liver                             liver           healthy  GSE159977                             scimilarity  nan     nan                         473      4877555.0     13370    33200.636271185205  0.9498326621251537  0.8392029926705806  0.8469259328198118\n",
+      "agg_7997  non-classical monocyte  liver                             liver           healthy  GSE163650                             scimilarity  nan     nan                         10       96148.0       6782     24958.605923030595  0.844996457443227   0.7380858347163884  0.7209649786125333\n",
+      "agg_8007  non-classical monocyte  lung                              lung            healthy  5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         576      3313836.0     12566    30969.255628748215  0.9438879246302309  0.8444989044016609  0.8358111974870654\n",
+      "agg_8008  non-classical monocyte  lung                              lung            healthy  DS000011735                           scimilarity  nan     nan                         169      779577.0      12885    36247.76234710614   0.8808886302153663  0.8102555656654346  0.7948721237235893\n",
+      "agg_8009  non-classical monocyte  lung                              lung            healthy  GSE128033                             scimilarity  nan     nan                         79       343546.0      9330     27406.22701580364   0.9035509606166174  0.7968173004217675  0.7863212826693502\n",
+      "agg_8010  non-classical monocyte  lung                              lung            healthy  GSE128169                             scimilarity  nan     nan                         276      2433151.0     12769    32027.902610765417  0.9547437308289124  0.8449880167209973  0.8438649522788478\n",
+      "agg_8011  non-classical monocyte  lung                              lung            healthy  GSE132771                             scimilarity  nan     nan                         37       151922.0      7860     26295.743163049112  0.8838770944570796  0.790431305730734   0.7607028661311058\n",
+      "agg_8012  non-classical monocyte  lung                              lung            healthy  GSE169471                             scimilarity  nan     nan                         27       103204.0      6854     23947.65597317191   0.8348392917103126  0.7488344663849291  0.7237935010019484\n",
+      "agg_8024  non-classical monocyte  lung parenchyma                   lung            healthy  GSE158127                             scimilarity  nan     nan                         309      1788828.0     12136    32078.56380429425   0.9418105467635777  0.8295808338281161  0.831837004521098\n",
+      "agg_8026  non-classical monocyte  muscle tissue                     muscle          healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         321      23778338.0    13169    27977.08919973388   0.8814221140322411  0.7869360982600341  0.7677337716492793\n",
+      "agg_8034  non-classical monocyte  right cardiac atrium              heart           healthy  b52eb423-5d0d-4645-b217-e1c6d38b2e72  scimilarity  nan     nan                         70       408965.0      10146    29746.121058681525  0.9252128588702657  0.8186384457806745  0.8103901461358153\n",
+      "agg_8036  non-classical monocyte  spleen                            spleen          healthy  4d74781b-8186-4c9a-b659-ff4dc4601d91  scimilarity  nan     nan                         336      1586973.0     11934    30580.798338873254  0.9471354298985436  0.8378586626071394  0.8322814736104384\n",
+      "agg_8039  non-classical monocyte  thoracic lymph node               lymph node      healthy  62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         1950     18888557.0    15221    33581.76389036113   0.9559331607078543  0.8537562341521224  0.8469325123405803\n",
+      "agg_8040  non-classical monocyte  thymus                            thymus          healthy  62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         68       441502.0      10301    31247.452676451114  0.917407487628822   0.8223254514108821  0.8197270854172879\n",
+      "agg_8041  non-classical monocyte  trachea                           airway          healthy  03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         12       97840.0       7863     29204.083428972655  0.8702163087134754  0.7685799000235117  0.7716054237714325\n",
+      "agg_8042  non-classical monocyte  trachea                           airway          healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         16       534304.0      8970     24807.157880956805  0.8232311272852844  0.7180336962121132  0.7003798955542161\n",
+      "agg_8044  non-classical monocyte  transverse colon                  gut             healthy  62ef75e4-cbea-454e-a0ce-998ec40223d3  scimilarity  nan     nan                         135      908452.0      11380    32804.296806012215  0.933818206129888   0.8353788654625934  0.8345447172426862\n",
+      "agg_8045  non-classical monocyte  upper lobe of lung                lung            healthy  GSE169471                             scimilarity  nan     nan                         32       143341.0      7269     24395.316476390286  0.8673226900308589  0.7523857428733156  0.7403920706432944\n",
+      "agg_8046  non-classical monocyte  vasculature                       vasculature     healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity  nan     nan                         987      10267209.0    15019    35019.47682838501   0.9379668399645168  0.8304128458333673  0.8283221722893935\n"
      ]
     },
     {
@@ -1606,31 +2174,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:29:35.059952Z",
-     "iopub.status.busy": "2025-11-21T06:29:35.059766Z",
-     "iopub.status.idle": "2025-11-21T06:29:49.968177Z",
-     "shell.execute_reply": "2025-11-21T06:29:49.967575Z"
-    }
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\r\n"
+      "          cell_type           tissue  organ  disease  study                                 dataset      region  subregion  celltype_coarse  n_cells  total_counts  n_genes  size_factor         train_pearson       val_pearson         test_pearson\n",
+      "agg_4733  classical monocyte  blood   blood  healthy  03f821b4-87be-4ff4-b65a-b5fc00061da7  scimilarity  nan     nan                         32464    109280914.0   16158    33646.02843110038   0.9568728712803031  0.8545324533535094  0.8487445540580735\n",
+      "agg_4734  classical monocyte  blood   blood  healthy  436154da-bcf1-4130-9c8b-120ff9a888f2  scimilarity  nan     nan                         76800    206490628.0   16683    30736.453324546856  0.955313650235467   0.8494127267867799  0.84210567312908\n",
+      "agg_4735  classical monocyte  blood   blood  healthy  5d445965-6f1a-4b68-ba3a-b8f765155d3a  scimilarity  nan     nan                         1044     3638976.0     12306    30542.8977364245    0.9465384578921433  0.851991795617166   0.8308191715256182\n",
+      "agg_4736  classical monocyte  blood   blood  healthy  DS000010023                           scimilarity  nan     nan                         243      362606.0      8414     25201.261024165953  0.8865446516098515  0.7621391376670988  0.7681818769796727\n",
+      "agg_4737  classical monocyte  blood   blood  healthy  GSE122703                             scimilarity  nan     nan                         18       83417.0       7546     28194.725173315186  0.859225612396475   0.7640890699253299  0.7443681539461423\n",
+      "agg_4738  classical monocyte  blood   blood  healthy  GSE130117                             scimilarity  nan     nan                         2017     7130588.0     13535    33078.53692191542   0.9553673450160365  0.851402109626239   0.8385936100758409\n",
+      "agg_4739  classical monocyte  blood   blood  healthy  GSE132802                             scimilarity  nan     nan                         1601     9955248.0     13132    32063.630951743195  0.9478882791739611  0.8391025143866828  0.8303465877530952\n",
+      "agg_4740  classical monocyte  blood   blood  healthy  GSE139324                             scimilarity  nan     nan                         2333     8331045.0     13985    31135.881287246768  0.9608208780142045  0.8473885992448625  0.8432790193723467\n",
+      "agg_4741  classical monocyte  blood   blood  healthy  GSE145809                             scimilarity  nan     nan                         69       245221.0      8962     29135.67197629852   0.8825701041728526  0.7811799267734735  0.7818647625179129\n",
+      "agg_4742  classical monocyte  blood   blood  healthy  GSE149313                             scimilarity  nan     nan                         2420     6974751.0     13143    29560.496854576566  0.9574598613513423  0.8505290963248237  0.8379199735887167\n",
+      "agg_4743  classical monocyte  blood   blood  healthy  GSE153421                             scimilarity  nan     nan                         3691     15561725.0    14569    34377.465875728165  0.9636686704566925  0.8576434473562725  0.8511814190737197\n",
+      "agg_4744  classical monocyte  blood   blood  healthy  GSE156989                             scimilarity  nan     nan                         13554    160011485.0   16915    34135.439844737564  0.9640667421350761  0.8577967800377495  0.8517975138366085\n",
+      "agg_4745  classical monocyte  blood   blood  healthy  GSE157829                             scimilarity  nan     nan                         1619     6957811.0     13507    30199.39288988673   0.9484019976492215  0.8436979316400604  0.8347196616710685\n",
+      "agg_4746  classical monocyte  blood   blood  healthy  GSE159113                             scimilarity  nan     nan                         1025     6298250.0     12083    27477.50809897617   0.9078020151513733  0.8121457150205226  0.7980372877810575\n",
+      "agg_4747  classical monocyte  blood   blood  healthy  GSE161329                             scimilarity  nan     nan                         5654     25653579.0    14349    28848.0539929647    0.9549801428956252  0.8450430950674043  0.8406188789518544\n",
+      "agg_4748  classical monocyte  blood   blood  healthy  GSE161738                             scimilarity  nan     nan                         2676     13801473.0    12825    33337.477050230416  0.9541962906717452  0.8512846409758499  0.8485408028961247\n",
+      "agg_4749  classical monocyte  blood   blood  healthy  GSE163668                             scimilarity  nan     nan                         2644     10486314.0    14049    33786.96584264489   0.9597801578342394  0.8560775485935677  0.8512149509551471\n",
+      "agg_4750  classical monocyte  blood   blood  healthy  GSE166992                             scimilarity  nan     nan                         7501     28033216.0    15079    33455.367364577316  0.9622273594219685  0.8558958139235102  0.8495571689751152\n",
+      "agg_4751  classical monocyte  blood   blood  healthy  GSE167363                             scimilarity  nan     nan                         3135     14722635.0    14375    29977.24002819913   0.942417448875388   0.8368071803109702  0.8258536430202982\n",
+      "agg_4752  classical monocyte  blood   blood  healthy  GSE168710                             scimilarity  nan     nan                         16484    104881872.0   16223    34107.336261357574  0.9398282119039322  0.8424821834537695  0.8372971004604842\n",
+      "agg_4753  classical monocyte  blood   blood  healthy  GSE168732                             scimilarity  nan     nan                         770      2548822.0     12508    33411.30103713399   0.9552513581030765  0.8508279875038706  0.847461536110767\n",
+      "agg_4754  classical monocyte  blood   blood  healthy  b0cf0afa-ec40-4d65-b570-ed4ceacc6813  scimilarity  nan     nan                         40975    300555227.0   15784    35938.85772500803   0.9622425892039956  0.853424173800979   0.8508714303589978\n",
+      "agg_4755  classical monocyte  blood   blood  healthy  ddfad306-714d-4cc0-9985-d9072820c530  scimilarity  nan     nan                         8827     36073928.0    15131    33208.591584008376  0.9546118779961532  0.8543086616569785  0.8462739374830107\n"
      ]
     },
     {
@@ -1707,56 +2281,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:29:49.970038Z",
-     "iopub.status.busy": "2025-11-21T06:29:49.969871Z",
-     "iopub.status.idle": "2025-11-21T06:29:50.102142Z",
-     "shell.execute_reply": "2025-11-21T06:29:50.101542Z"
-    }
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cat: ../tests/data/seqs.fasta: No such file or directory\r\n"
+      ">CD68|gene_mask_start=163840|gene_mask_end=166460\n",
+      "CTCTGCAGAGAGCGAGGACGGTGTGTCTGCCAGCGCCTTTGACTTCACTGTCTCCAACTTTGTGGACAACCTGTATGGCTACCCGGAAGGCAAGGATGTGCTTCGGGAGACCATCAAGTTTATGTACACAGACTGGGCCGACCGGGACAATGGCGAAATGCGCCGCAAAACCCTGCTGGCGCTCTTTACTGACCACCAAT\n",
+      ">SPI1|gene_mask_start=163840|gene_mask_end=187556\n",
+      "TGCCACTTTTAGATATGTTCATGGGTGCAGATACGGCTTTATTTATTTGAGACAGAGTTTCACTCTTGTTGCCCAGGGTGGAGTGCAGTGGTGCGATCTCAGCTCACTGCAGCCTTCGCCTCCCGGGTTGAAGCGATTCTTCTGCCTCAACCTCGAGTAGCTGGGATTATAGGCACCTGCCAGCATGCCTGGCTAATTTT\n"
      ]
     }
    ],
    "source": [
-    "! cat ../tests/data/seqs.fasta | cut -c 1-200"
+    "! cat ../../tests/data/seqs.fasta | cut -c 1-200"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:29:50.103740Z",
-     "iopub.status.busy": "2025-11-21T06:29:50.103580Z",
-     "iopub.status.idle": "2025-11-21T06:30:36.906607Z",
-     "shell.execute_reply": "2025-11-21T06:30:36.905975Z"
-    }
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "decima - INFO - Using device: 0\r\n",
-      "decima - INFO - Loading model v1_rep0 and metadata to compute attributions...\r\n"
+      "decima - INFO - Using device: 0\n",
+      "decima - INFO - Loading model v1_rep0 and metadata to compute attributions...\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.23s/it]\n",
+      "decima - INFO - Saving sequences...\n",
+      "Saving sequences...: 2it [00:00, 11618.57it/s]\n",
+      "decima - INFO - Loading model and metadata to compute attributions...\n",
+      "decima - INFO - No genes provided, using all 2 genes in the attribution files.\n",
+      "Computing recursive seqlet calling...: 100%|█████| 2/2 [00:00<00:00, 723.72it/s]\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "\u001b[0m\u001b[0m\u001b[0m"
      ]
     },
     {
@@ -1807,7 +2370,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|                          | 0/2 [00:00<?, ?it/s]"
      ]
     },
@@ -1815,7 +2378,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:  50%|█████████         | 1/2 [00:01<00:01,  1.38s/it]"
      ]
     },
@@ -1823,7 +2386,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.00it/s]"
      ]
     },
@@ -1831,7 +2394,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████████████| 2/2 [00:02<00:00,  1.08s/it]\r\n"
      ]
     },
@@ -1840,8 +2403,8 @@
      "output_type": "stream",
      "text": [
       "decima - INFO - Saving sequences...\r\n",
-      "\r",
-      "Saving sequences...: 0it [00:00, ?it/s]\r",
+      "\r\n",
+      "Saving sequences...: 0it [00:00, ?it/s]\r\n",
       "Saving sequences...: 2it [00:00, 10965.50it/s]\r\n",
       "decima - INFO - Loading model and metadata to compute attributions...\r\n"
      ]
@@ -1866,8 +2429,8 @@
      "output_type": "stream",
      "text": [
       "decima - INFO - No genes provided, using all 2 genes in the attribution files.\r\n",
-      "\r",
-      "Computing recursive seqlet calling...:   0%|              | 0/2 [00:00<?, ?it/s]\r",
+      "\r\n",
+      "Computing recursive seqlet calling...:   0%|              | 0/2 [00:00<?, ?it/s]\r\n",
       "Computing recursive seqlet calling...: 100%|█████| 2/2 [00:00<00:00, 597.44it/s]\r\n"
      ]
     },
@@ -1940,27 +2503,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:30:36.908689Z",
-     "iopub.status.busy": "2025-11-21T06:30:36.908504Z",
-     "iopub.status.idle": "2025-11-21T06:30:37.042212Z",
-     "shell.execute_reply": "2025-11-21T06:30:37.041706Z"
-    }
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "example/output_custom_seqs.attributions.bigwig\r\n",
-      "example/output_custom_seqs.attributions.h5\r\n",
-      "example/output_custom_seqs.motifs.tsv\r\n",
-      "example/output_custom_seqs.seqlets.bed\r\n",
-      "example/output_custom_seqs.seqs.fasta\r\n",
-      "example/output_custom_seqs.seqs.fasta.fai\r\n",
-      "example/output_custom_seqs.warnings.qc.log\r\n"
+      "example/output_custom_seqs.attributions.bigwig\n",
+      "example/output_custom_seqs.attributions.h5\n",
+      "example/output_custom_seqs.motifs.tsv\n",
+      "example/output_custom_seqs.seqlets.bed\n",
+      "example/output_custom_seqs.seqs.fasta\n",
+      "example/output_custom_seqs.seqs.fasta.fai\n",
+      "example/output_custom_seqs.warnings.qc.log\n"
      ]
     }
    ],
@@ -1977,75 +2533,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:30:37.043938Z",
-     "iopub.status.busy": "2025-11-21T06:30:37.043790Z",
-     "iopub.status.idle": "2025-11-21T06:30:37.174697Z",
-     "shell.execute_reply": "2025-11-21T06:30:37.174145Z"
-    }
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "example/output_classical_monoctypes.attributions.bigwig\r\n",
-      "example/output_classical_monoctypes.attributions.h5\r\n",
-      "example/output_classical_monoctypes.motifs.tsv\r\n",
-      "example/output_classical_monoctypes.seqlets.bed\r\n",
-      "example/output_classical_monoctypes.warnings.qc.log\r\n"
+      "example/output_classical_monocytes.attributions.bigwig\n",
+      "example/output_classical_monocytes.attributions.h5\n",
+      "example/output_classical_monocytes.motifs.tsv\n",
+      "example/output_classical_monocytes.seqlets.bed\n",
+      "example/output_classical_monocytes.warnings.qc.log\n"
      ]
     }
    ],
    "source": [
-    "! ls example/output_classical_monoctypes.*"
+    "! ls example/output_classical_monocytes.*"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:30:37.176265Z",
-     "iopub.status.busy": "2025-11-21T06:30:37.176119Z",
-     "iopub.status.idle": "2025-11-21T06:30:51.894902Z",
-     "shell.execute_reply": "2025-11-21T06:30:51.894345Z"
-    }
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
-      "  warnings.warn(\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Loading attributions and sequences...:   0%|          | 0/1 [00:00<?, ?it/s]"
+      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00, 245.22it/s]\n"
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00, 432.31it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "seqs: [[[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 1. ... 0. 0. 0.]\n",
+      "  [0. 1. 0. ... 1. 0. 1.]\n",
+      "  [1. 0. 0. ... 0. 1. 0.]]]\n",
+      "attrs: [[[-5.10011851e-05 -3.68037290e-05  7.21973493e-06 ... -1.40866014e-05\n",
+      "   -5.01500017e-06 -6.24567565e-06]\n",
+      "  [-5.10011851e-05 -3.68037290e-05 -2.16592048e-05 ... -1.40866014e-05\n",
+      "   -5.01500017e-06 -6.24567565e-06]\n",
+      "  [-5.10011851e-05  1.10411187e-04  7.21973493e-06 ...  4.22598041e-05\n",
+      "   -5.01500017e-06  1.87370269e-05]\n",
+      "  [ 1.53003555e-04 -3.68037290e-05  7.21973493e-06 ... -1.40866014e-05\n",
+      "    1.50450005e-05 -6.24567565e-06]]]\n"
      ]
     },
     {
@@ -2080,7 +2614,7 @@
    "source": [
     "from decima.interpret.attributions import AttributionResult\n",
     "\n",
-    "with AttributionResult(\"example/output_classical_monoctypes.attributions.h5\") as ar:\n",
+    "with AttributionResult(\"example/output_classical_monocytes.attributions.h5\") as ar:\n",
     "    seqs, attrs = ar.load([\"SPI1\"])\n",
     "    print(\"seqs:\", seqs)\n",
     "    print(\"attrs:\", attrs)"
@@ -2097,30 +2631,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:30:51.897052Z",
-     "iopub.status.busy": "2025-11-21T06:30:51.896450Z",
-     "iopub.status.idle": "2025-11-21T06:30:55.850627Z",
-     "shell.execute_reply": "2025-11-21T06:30:55.850006Z"
-    }
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -2137,25 +2650,18 @@
     }
    ],
    "source": [
-    "with AttributionResult(\"example/output_classical_monoctypes.attributions.h5\") as ar:\n",
+    "with AttributionResult(\"example/output_classical_monocytes.attributions.h5\") as ar:\n",
     "    attribution = ar.load_attribution(\"SPI1\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:30:55.852529Z",
-     "iopub.status.busy": "2025-11-21T06:30:55.852363Z",
-     "iopub.status.idle": "2025-11-21T06:30:56.743158Z",
-     "shell.execute_reply": "2025-11-21T06:30:56.742602Z"
-    }
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x200 with 1 Axes>"
       ]
@@ -2173,27 +2679,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:30:56.744352Z",
-     "iopub.status.busy": "2025-11-21T06:30:56.744216Z",
-     "iopub.status.idle": "2025-11-21T06:31:03.814731Z",
-     "shell.execute_reply": "2025-11-21T06:31:03.814125Z"
-    }
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x14bfa1e9bf90>"
+      ]
      },
+     "execution_count": 25,
      "metadata": {
       "image/png": {
        "height": 200,
        "width": 1000
       }
      },
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2202,15 +2705,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:31:03.816125Z",
-     "iopub.status.busy": "2025-11-21T06:31:03.815965Z",
-     "iopub.status.idle": "2025-11-21T06:31:03.905305Z",
-     "shell.execute_reply": "2025-11-21T06:31:03.904884Z"
-    }
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import torch\n",
@@ -2223,15 +2719,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:31:03.906863Z",
-     "iopub.status.busy": "2025-11-21T06:31:03.906719Z",
-     "iopub.status.idle": "2025-11-21T06:31:03.908718Z",
-     "shell.execute_reply": "2025-11-21T06:31:03.908328Z"
-    }
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [],
    "source": [
     "spi1_cell_types = [\n",
@@ -2245,28 +2734,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:31:03.910006Z",
-     "iopub.status.busy": "2025-11-21T06:31:03.909877Z",
-     "iopub.status.idle": "2025-11-21T06:32:07.465527Z",
-     "shell.execute_reply": "2025-11-21T06:32:07.464802Z"
-    }
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 1/1 [00:01<00:00,  1.63s/it]\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 1/1 [00:01<00:00,  1.13s/it]\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 1/1 [00:01<00:00,  1.15s/it]\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 1/1 [00:01<00:00,  1.21s/it]\n",
+      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:00<00:00,  1.01it/s]\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n"
      ]
     },
     {
@@ -2308,7 +2793,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
@@ -2316,7 +2801,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:01<00:00,  1.51s/it]"
      ]
     },
@@ -2324,7 +2809,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:01<00:00,  1.55s/it]"
      ]
     },
@@ -2388,7 +2873,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
@@ -2396,7 +2881,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.10it/s]"
      ]
     },
@@ -2404,7 +2889,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.04it/s]"
      ]
     },
@@ -2468,7 +2953,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
@@ -2476,7 +2961,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.07it/s]"
      ]
     },
@@ -2484,7 +2969,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.01it/s]"
      ]
     },
@@ -2548,7 +3033,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
@@ -2556,7 +3041,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.08it/s]"
      ]
     },
@@ -2564,7 +3049,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.02it/s]"
      ]
     },
@@ -2621,7 +3106,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing recursive seqlet calling...:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
@@ -2629,7 +3114,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]"
      ]
     },
@@ -2637,7 +3122,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]"
      ]
     },
@@ -2668,7 +3153,7 @@
    ],
    "source": [
     "predict_attributions_seqlet_calling(\n",
-    "    output_prefix=\"example/attrs_SP1I_monoctypes\",\n",
+    "    output_prefix=\"example/attrs_SP1I_monocytes\",\n",
     "    genes=[\"SPI1\"],\n",
     "    tasks=f\"cell_type in {spi1_cell_types}\",\n",
     "    device=device,\n",
@@ -2698,67 +3183,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:07.467872Z",
-     "iopub.status.busy": "2025-11-21T06:32:07.467672Z",
-     "iopub.status.idle": "2025-11-21T06:32:07.903757Z",
-     "shell.execute_reply": "2025-11-21T06:32:07.903194Z"
-    }
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '../tests/data/seqs.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df_seqs = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m../tests/data/seqs.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m df_seqs\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/decima2/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m   1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m   1014\u001b[39m     dialect,\n\u001b[32m   1015\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1022\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m   1023\u001b[39m )\n\u001b[32m   1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/decima2/lib/python3.11/site-packages/pandas/io/parsers/readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    623\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/decima2/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1617\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/decima2/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1878\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1879\u001b[39m         mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1881\u001b[39m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1882\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1883\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1884\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1885\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1886\u001b[39m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1887\u001b[39m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1888\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/decima2/lib/python3.11/site-packages/pandas/io/common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m    868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    869\u001b[39m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m    870\u001b[39m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m    871\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m    872\u001b[39m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m         handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m    874\u001b[39m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    875\u001b[39m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    876\u001b[39m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    877\u001b[39m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    878\u001b[39m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    879\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    880\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    881\u001b[39m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m    882\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
-      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '../tests/data/seqs.csv'"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>seq</th>\n",
+       "      <th>gene_mask_start</th>\n",
+       "      <th>gene_mask_end</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CD68</th>\n",
+       "      <td>CTCTGCAGAGAGCGAGGACGGTGTGTCTGCCAGCGCCTTTGACTTC...</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>166460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SPI1</th>\n",
+       "      <td>TGCCACTTTTAGATATGTTCATGGGTGCAGATACGGCTTTATTTAT...</td>\n",
+       "      <td>163840</td>\n",
+       "      <td>187556</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    seq  gene_mask_start  \\\n",
+       "name                                                                       \n",
+       "CD68  CTCTGCAGAGAGCGAGGACGGTGTGTCTGCCAGCGCCTTTGACTTC...           163840   \n",
+       "SPI1  TGCCACTTTTAGATATGTTCATGGGTGCAGATACGGCTTTATTTAT...           163840   \n",
+       "\n",
+       "      gene_mask_end  \n",
+       "name                 \n",
+       "CD68         166460  \n",
+       "SPI1         187556  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "df_seqs = pd.read_csv(\"../tests/data/seqs.csv\", index_col=0)\n",
+    "df_seqs = pd.read_csv(\"../../tests/data/seqs.csv\", index_col=0)\n",
     "df_seqs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:07.905316Z",
-     "iopub.status.busy": "2025-11-21T06:32:07.905178Z",
-     "iopub.status.idle": "2025-11-21T06:32:07.926685Z",
-     "shell.execute_reply": "2025-11-21T06:32:07.926250Z"
-    }
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'df_seqs' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[29]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m predict_attributions_seqlet_calling(\n\u001b[32m      2\u001b[39m     output_prefix=\u001b[33m\"\u001b[39m\u001b[33mexample/attrs_custom_seqs_monoctypes\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m     seqs=\u001b[43mdf_seqs\u001b[49m,  \u001b[38;5;66;03m# <-- custom sequences\u001b[39;00m\n\u001b[32m      4\u001b[39m     tasks=\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mcell_type in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mspi1_cell_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m      5\u001b[39m     device=device,\n\u001b[32m      6\u001b[39m )\n\u001b[32m      7\u001b[39m get_ipython().system(\u001b[33m'\u001b[39m\u001b[33m ls attrs_custom_seqs_monoctypes\u001b[39m\u001b[33m'\u001b[39m)\n",
-      "\u001b[31mNameError\u001b[39m: name 'df_seqs' is not defined"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 2/2 [00:01<00:00,  1.01it/s]\n",
+      "Saving sequences...: 2it [00:00, 11320.66it/s]\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 2/2 [00:01<00:00,  1.02it/s]\n",
+      "Saving sequences...: 2it [00:00, 11538.66it/s]\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 2/2 [00:01<00:00,  1.01it/s]\n",
+      "Saving sequences...: 2it [00:00, 7294.44it/s]\n",
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 2/2 [00:01<00:00,  1.01it/s]\n",
+      "Saving sequences...: 2it [00:00, 8042.77it/s]\n",
+      "Computing recursive seqlet calling...: 100%|██████████| 2/2 [00:00<00:00, 1294.34it/s]\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ls: cannot access 'attrs_custom_seqs_monoctypes': No such file or directory\n"
      ]
     }
    ],
    "source": [
     "predict_attributions_seqlet_calling(\n",
-    "    output_prefix=\"example/attrs_custom_seqs_monoctypes\",\n",
+    "    output_prefix=\"example/attrs_custom_seqs_monocytes\",\n",
     "    seqs=df_seqs,  # <-- custom sequences\n",
     "    tasks=f\"cell_type in {spi1_cell_types}\",\n",
     "    device=device,\n",
@@ -2768,15 +3305,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:07.927982Z",
-     "iopub.status.busy": "2025-11-21T06:32:07.927858Z",
-     "iopub.status.idle": "2025-11-21T06:32:07.931232Z",
-     "shell.execute_reply": "2025-11-21T06:32:07.930844Z"
-    }
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2784,7 +3314,7 @@
        "524288"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2800,15 +3330,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:07.932508Z",
-     "iopub.status.busy": "2025-11-21T06:32:07.932376Z",
-     "iopub.status.idle": "2025-11-21T06:32:08.108325Z",
-     "shell.execute_reply": "2025-11-21T06:32:08.104954Z"
-    }
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2816,7 +3339,7 @@
        "torch.Size([1, 5, 524288])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2836,28 +3359,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:08.109954Z",
-     "iopub.status.busy": "2025-11-21T06:32:08.109816Z",
-     "iopub.status.idle": "2025-11-21T06:32:34.000117Z",
-     "shell.execute_reply": "2025-11-21T06:32:33.999519Z"
-    }
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
+      "/home/lala8/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "Computing attributions...: 100%|██████████| 1/1 [00:01<00:00,  1.18s/it]\n",
+      "Saving sequences...: 1it [00:00, 7639.90it/s]\n",
+      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:00<00:00, 1247.19it/s]\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n"
      ]
     },
     {
@@ -2899,7 +3412,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
@@ -2907,7 +3420,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.08it/s]"
      ]
     },
@@ -2915,7 +3428,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.02it/s]"
      ]
     },
@@ -2930,7 +3443,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Saving sequences...: 0it [00:00, ?it/s]"
      ]
     },
@@ -2938,7 +3451,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Saving sequences...: 1it [00:00, 8525.01it/s]"
      ]
     },
@@ -2974,7 +3487,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing recursive seqlet calling...:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
@@ -2982,7 +3495,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:00<00:00, 1403.25it/s]"
      ]
     },
@@ -3013,7 +3526,7 @@
    ],
    "source": [
     "predict_attributions_seqlet_calling(\n",
-    "    output_prefix=\"example/attrs_custom_tensors_monoctypes\",\n",
+    "    output_prefix=\"example/attrs_custom_tensors_monocytes\",\n",
     "    seqs=seqs,  # <-- custom sequences as torch.Tensor where (batch_size, 5, seq_len), second dimension is one-hot encoded sequence and binary mask for the gene\n",
     "    tasks=f\"cell_type in {spi1_cell_types}\",\n",
     "    device=device,\n",
@@ -3024,39 +3537,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:34.001527Z",
-     "iopub.status.busy": "2025-11-21T06:32:34.001361Z",
-     "iopub.status.idle": "2025-11-21T06:32:34.213274Z",
-     "shell.execute_reply": "2025-11-21T06:32:34.212586Z"
-    }
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "example/attrs_custom_tensors_monoctypes.attributions.bigwig\r\n",
-      "example/attrs_custom_tensors_monoctypes.attributions.h5\r\n",
-      "example/attrs_custom_tensors_monoctypes.motifs.tsv\r\n",
-      "example/attrs_custom_tensors_monoctypes.seqlets.bed\r\n",
-      "example/attrs_custom_tensors_monoctypes.seqs.fasta\r\n",
-      "example/attrs_custom_tensors_monoctypes.seqs.fasta.fai\r\n",
-      "example/attrs_custom_tensors_monoctypes.warnings.qc.log\r\n"
+      "example/attrs_custom_tensors_monocytes.attributions.bigwig\n",
+      "example/attrs_custom_tensors_monocytes.attributions.h5\n",
+      "example/attrs_custom_tensors_monocytes.motifs.tsv\n",
+      "example/attrs_custom_tensors_monocytes.seqlets.bed\n",
+      "example/attrs_custom_tensors_monocytes.seqs.fasta\n",
+      "example/attrs_custom_tensors_monocytes.seqs.fasta.fai\n",
+      "example/attrs_custom_tensors_monocytes.warnings.qc.log\n"
      ]
     }
    ],
    "source": [
-    "! ls example/attrs_custom_tensors_monoctypes*"
+    "! ls example/attrs_custom_tensors_monocytes*"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Advance Developer API"
+    "### Advanced Developer API"
    ]
   },
   {
@@ -3068,55 +3574,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:34.215273Z",
-     "iopub.status.busy": "2025-11-21T06:32:34.215105Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.862705Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.862082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:01.9 (1639.9MB/s)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from decima import DecimaResult\n",
-    "\n",
-    "result = DecimaResult.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.864379Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.864221Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.882125Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.881599Z"
-    }
-   },
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3428,7 +3887,7 @@
        "[325 rows x 16 columns]"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3446,15 +3905,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.883264Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.883127Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.885853Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.885430Z"
-    }
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3465,7 +3917,7 @@
        "    layers: 'preds', 'v1_rep0', 'v1_rep1', 'v1_rep2', 'v1_rep3'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3483,15 +3935,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.887047Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.886918Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.897015Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.896599Z"
-    }
-   },
+   "execution_count": 39,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3829,7 +4274,7 @@
        "[8856 rows x 16 columns]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3847,15 +4292,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.898318Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.898189Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.908345Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.907862Z"
-    }
-   },
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4228,7 +4666,7 @@
        "[18457 rows x 20 columns]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4246,15 +4684,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.909461Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.909331Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.912200Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.911806Z"
-    }
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4267,7 +4698,7 @@
        "      dtype='object', length=18457)"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4285,15 +4716,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.913527Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.913396Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.916112Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.915683Z"
-    }
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4306,7 +4730,7 @@
        "      dtype='object', length=8856)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4324,15 +4748,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.917242Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.917117Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.931953Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.931543Z"
-    }
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4425,7 +4842,7 @@
        "[8856 rows x 1 columns]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4443,15 +4860,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.933257Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.933128Z",
-     "iopub.status.idle": "2025-11-21T06:32:37.946479Z",
-     "shell.execute_reply": "2025-11-21T06:32:37.946096Z"
-    }
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4823,7 +5233,7 @@
        "[8856 rows x 18457 columns]"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4834,43 +5244,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:32:37.947703Z",
-     "iopub.status.busy": "2025-11-21T06:32:37.947568Z",
-     "iopub.status.idle": "2025-11-21T06:33:00.525878Z",
-     "shell.execute_reply": "2025-11-21T06:33:00.525331Z"
-    }
-   },
+   "execution_count": 45,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m A graphql request initiated by the public wandb API timed out (timeout=19 sec). Create a new API with an integer timeout larger than 19, e.g., `api = wandb.Api(timeout=29)` to increase the graphql timeout.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:00.7 (1008.5MB/s)\n"
-     ]
+     "data": {
+      "text/plain": [
+       "DecimaResult(anndata=AnnData object with n_obs × n_vars = 8856 × 18457\n",
+       "    obs: 'cell_type', 'tissue', 'organ', 'disease', 'study', 'dataset', 'region', 'subregion', 'celltype_coarse', 'n_cells', 'total_counts', 'n_genes', 'size_factor', 'train_pearson', 'val_pearson', 'test_pearson'\n",
+       "    var: 'chrom', 'start', 'end', 'strand', 'gene_type', 'frac_nan', 'mean_counts', 'n_tracks', 'gene_start', 'gene_end', 'gene_length', 'gene_mask_start', 'gene_mask_end', 'frac_N', 'fold', 'dataset', 'gene_id', 'pearson', 'size_factor_pearson', 'ensembl_canonical_tss'\n",
+       "    layers: 'preds', 'v1_rep0', 'v1_rep1', 'v1_rep2', 'v1_rep3')"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "data": {
@@ -4906,15 +5294,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:00.527547Z",
-     "iopub.status.busy": "2025-11-21T06:33:00.527394Z",
-     "iopub.status.idle": "2025-11-21T06:33:01.199621Z",
-     "shell.execute_reply": "2025-11-21T06:33:01.198754Z"
-    }
-   },
+   "execution_count": 46,
+   "metadata": {},
    "outputs": [],
    "source": [
     "attrs = result.attributions(\n",
@@ -4933,15 +5314,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:01.200991Z",
-     "iopub.status.busy": "2025-11-21T06:33:01.200846Z",
-     "iopub.status.idle": "2025-11-21T06:33:04.240855Z",
-     "shell.execute_reply": "2025-11-21T06:33:04.240273Z"
-    }
-   },
+   "execution_count": 47,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4978,26 +5352,26 @@
        "      <td>pos.SPI1@37</td>\n",
        "      <td>163877</td>\n",
        "      <td>163902</td>\n",
-       "      <td>12.817252</td>\n",
-       "      <td>2.186883e-11</td>\n",
+       "      <td>12.821853</td>\n",
+       "      <td>2.186188e-11</td>\n",
        "      <td>37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>pos.SPI1@-121</td>\n",
-       "      <td>163719</td>\n",
-       "      <td>163744</td>\n",
-       "      <td>5.595659</td>\n",
-       "      <td>1.899081e-05</td>\n",
-       "      <td>-121</td>\n",
+       "      <td>pos.SPI1@-122</td>\n",
+       "      <td>163718</td>\n",
+       "      <td>163743</td>\n",
+       "      <td>5.638458</td>\n",
+       "      <td>2.154073e-05</td>\n",
+       "      <td>-122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>pos.SPI1@-57</td>\n",
        "      <td>163783</td>\n",
        "      <td>163803</td>\n",
-       "      <td>9.307484</td>\n",
-       "      <td>3.054640e-05</td>\n",
+       "      <td>9.297145</td>\n",
+       "      <td>3.056442e-05</td>\n",
        "      <td>-57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -5005,8 +5379,8 @@
        "      <td>pos.SPI1@62</td>\n",
        "      <td>163902</td>\n",
        "      <td>163909</td>\n",
-       "      <td>1.281183</td>\n",
-       "      <td>3.068997e-05</td>\n",
+       "      <td>1.280417</td>\n",
+       "      <td>3.077221e-05</td>\n",
        "      <td>62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -5014,8 +5388,8 @@
        "      <td>pos.SPI1@-79</td>\n",
        "      <td>163761</td>\n",
        "      <td>163765</td>\n",
-       "      <td>0.833269</td>\n",
-       "      <td>6.109865e-05</td>\n",
+       "      <td>0.832793</td>\n",
+       "      <td>6.111014e-05</td>\n",
        "      <td>-79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -5028,73 +5402,73 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>neg.SPI1@443</td>\n",
-       "      <td>164283</td>\n",
-       "      <td>164293</td>\n",
-       "      <td>-0.717349</td>\n",
-       "      <td>4.916059e-04</td>\n",
-       "      <td>443</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>73</th>\n",
-       "      <td>neg.SPI1@23600</td>\n",
-       "      <td>187440</td>\n",
-       "      <td>187445</td>\n",
-       "      <td>-0.267438</td>\n",
-       "      <td>4.916059e-04</td>\n",
-       "      <td>23600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
        "      <td>neg.SPI1@32783</td>\n",
        "      <td>196623</td>\n",
        "      <td>196630</td>\n",
-       "      <td>-0.461813</td>\n",
-       "      <td>4.918151e-04</td>\n",
+       "      <td>-0.460830</td>\n",
+       "      <td>4.895238e-04</td>\n",
        "      <td>32783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>neg.SPI1@-21806</td>\n",
+       "      <td>142034</td>\n",
+       "      <td>142040</td>\n",
+       "      <td>-0.328370</td>\n",
+       "      <td>4.895238e-04</td>\n",
+       "      <td>-21806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>75</th>\n",
-       "      <td>neg.SPI1@1735</td>\n",
-       "      <td>165575</td>\n",
-       "      <td>165592</td>\n",
-       "      <td>-1.437498</td>\n",
-       "      <td>4.918151e-04</td>\n",
-       "      <td>1735</td>\n",
+       "      <td>neg.SPI1@151</td>\n",
+       "      <td>163991</td>\n",
+       "      <td>164000</td>\n",
+       "      <td>-0.559542</td>\n",
+       "      <td>4.931061e-04</td>\n",
+       "      <td>151</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>76</th>\n",
-       "      <td>neg.SPI1@31668</td>\n",
-       "      <td>195508</td>\n",
-       "      <td>195512</td>\n",
-       "      <td>-0.213403</td>\n",
-       "      <td>4.918151e-04</td>\n",
-       "      <td>31668</td>\n",
+       "      <td>neg.SPI1@-22</td>\n",
+       "      <td>163818</td>\n",
+       "      <td>163836</td>\n",
+       "      <td>-11.183208</td>\n",
+       "      <td>4.931061e-04</td>\n",
+       "      <td>-22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>neg.SPI1@-145187</td>\n",
+       "      <td>18653</td>\n",
+       "      <td>18657</td>\n",
+       "      <td>-0.209094</td>\n",
+       "      <td>4.931061e-04</td>\n",
+       "      <td>-145187</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>135 rows × 6 columns</p>\n",
+       "<p>133 rows × 6 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "              peak   start     end  attribution       p-value  from_tss\n",
-       "0      pos.SPI1@37  163877  163902    12.817252  2.186883e-11        37\n",
-       "1    pos.SPI1@-121  163719  163744     5.595659  1.899081e-05      -121\n",
-       "2     pos.SPI1@-57  163783  163803     9.307484  3.054640e-05       -57\n",
-       "3      pos.SPI1@62  163902  163909     1.281183  3.068997e-05        62\n",
-       "4     pos.SPI1@-79  163761  163765     0.833269  6.109865e-05       -79\n",
-       "..             ...     ...     ...          ...           ...       ...\n",
-       "72    neg.SPI1@443  164283  164293    -0.717349  4.916059e-04       443\n",
-       "73  neg.SPI1@23600  187440  187445    -0.267438  4.916059e-04     23600\n",
-       "74  neg.SPI1@32783  196623  196630    -0.461813  4.918151e-04     32783\n",
-       "75   neg.SPI1@1735  165575  165592    -1.437498  4.918151e-04      1735\n",
-       "76  neg.SPI1@31668  195508  195512    -0.213403  4.918151e-04     31668\n",
+       "                peak   start     end  attribution       p-value  from_tss\n",
+       "0        pos.SPI1@37  163877  163902    12.821853  2.186188e-11        37\n",
+       "1      pos.SPI1@-122  163718  163743     5.638458  2.154073e-05      -122\n",
+       "2       pos.SPI1@-57  163783  163803     9.297145  3.056442e-05       -57\n",
+       "3        pos.SPI1@62  163902  163909     1.280417  3.077221e-05        62\n",
+       "4       pos.SPI1@-79  163761  163765     0.832793  6.111014e-05       -79\n",
+       "..               ...     ...     ...          ...           ...       ...\n",
+       "73    neg.SPI1@32783  196623  196630    -0.460830  4.895238e-04     32783\n",
+       "74   neg.SPI1@-21806  142034  142040    -0.328370  4.895238e-04    -21806\n",
+       "75      neg.SPI1@151  163991  164000    -0.559542  4.931061e-04       151\n",
+       "76      neg.SPI1@-22  163818  163836   -11.183208  4.931061e-04       -22\n",
+       "77  neg.SPI1@-145187   18653   18657    -0.209094  4.931061e-04   -145187\n",
        "\n",
-       "[135 rows x 6 columns]"
+       "[133 rows x 6 columns]"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5105,15 +5479,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:04.242160Z",
-     "iopub.status.busy": "2025-11-21T06:33:04.242016Z",
-     "iopub.status.idle": "2025-11-21T06:33:04.251058Z",
-     "shell.execute_reply": "2025-11-21T06:33:04.250687Z"
-    }
-   },
+   "execution_count": 48,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5147,54 +5514,54 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>38</th>\n",
+       "      <th>44</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47216350</td>\n",
        "      <td>47216357</td>\n",
        "      <td>pos.SPI1@162219</td>\n",
-       "      <td>3.33494</td>\n",
+       "      <td>3.32194</td>\n",
        "      <td>.</td>\n",
-       "      <td>0.543797</td>\n",
+       "      <td>0.543324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>49</th>\n",
+       "      <th>52</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47257597</td>\n",
        "      <td>47257605</td>\n",
        "      <td>pos.SPI1@120971</td>\n",
-       "      <td>3.31931</td>\n",
+       "      <td>3.30576</td>\n",
        "      <td>.</td>\n",
-       "      <td>0.680714</td>\n",
+       "      <td>0.680858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>65</th>\n",
+       "      <th>63</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47257633</td>\n",
        "      <td>47257637</td>\n",
        "      <td>neg.SPI1@120939</td>\n",
-       "      <td>3.31455</td>\n",
+       "      <td>3.32386</td>\n",
        "      <td>.</td>\n",
-       "      <td>-0.221530</td>\n",
+       "      <td>-0.221565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>63</th>\n",
+       "      <th>61</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47257734</td>\n",
        "      <td>47257739</td>\n",
        "      <td>neg.SPI1@120837</td>\n",
-       "      <td>3.32086</td>\n",
+       "      <td>3.32558</td>\n",
        "      <td>.</td>\n",
-       "      <td>-0.273840</td>\n",
+       "      <td>-0.273827</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>43</th>\n",
+       "      <th>30</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47345731</td>\n",
        "      <td>47345736</td>\n",
        "      <td>neg.SPI1@32840</td>\n",
-       "      <td>3.35317</td>\n",
+       "      <td>3.37933</td>\n",
        "      <td>.</td>\n",
-       "      <td>-0.298483</td>\n",
+       "      <td>-0.296822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -5207,78 +5574,78 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>chr11</td>\n",
-       "      <td>47395483</td>\n",
-       "      <td>47395492</td>\n",
-       "      <td>neg.SPI1@-16916</td>\n",
-       "      <td>3.31094</td>\n",
-       "      <td>.</td>\n",
-       "      <td>-0.567760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
+       "      <th>36</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47400211</td>\n",
        "      <td>47400221</td>\n",
        "      <td>neg.SPI1@-21645</td>\n",
-       "      <td>3.35527</td>\n",
+       "      <td>3.36227</td>\n",
        "      <td>.</td>\n",
-       "      <td>-0.900000</td>\n",
+       "      <td>-0.900207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>37</th>\n",
+       "      <th>41</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47400225</td>\n",
        "      <td>47400235</td>\n",
        "      <td>neg.SPI1@-21659</td>\n",
-       "      <td>3.35844</td>\n",
+       "      <td>3.35864</td>\n",
        "      <td>.</td>\n",
-       "      <td>-0.729126</td>\n",
+       "      <td>-0.728760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>68</th>\n",
+       "      <th>74</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47400376</td>\n",
        "      <td>47400382</td>\n",
        "      <td>neg.SPI1@-21806</td>\n",
-       "      <td>3.31094</td>\n",
+       "      <td>3.31023</td>\n",
        "      <td>.</td>\n",
-       "      <td>-0.329538</td>\n",
+       "      <td>-0.328370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>58</th>\n",
+       "      <th>52</th>\n",
        "      <td>chr11</td>\n",
        "      <td>47400703</td>\n",
        "      <td>47400709</td>\n",
        "      <td>neg.SPI1@-22133</td>\n",
-       "      <td>3.33067</td>\n",
+       "      <td>3.34088</td>\n",
        "      <td>.</td>\n",
-       "      <td>-0.325769</td>\n",
+       "      <td>-0.327795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>47523759</td>\n",
+       "      <td>47523763</td>\n",
+       "      <td>neg.SPI1@-145187</td>\n",
+       "      <td>3.30706</td>\n",
+       "      <td>.</td>\n",
+       "      <td>-0.209094</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>135 rows × 7 columns</p>\n",
+       "<p>133 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    chrom     start       end             name    score strand  attribution\n",
-       "38  chr11  47216350  47216357  pos.SPI1@162219  3.33494      .     0.543797\n",
-       "49  chr11  47257597  47257605  pos.SPI1@120971  3.31931      .     0.680714\n",
-       "65  chr11  47257633  47257637  neg.SPI1@120939  3.31455      .    -0.221530\n",
-       "63  chr11  47257734  47257739  neg.SPI1@120837  3.32086      .    -0.273840\n",
-       "43  chr11  47345731  47345736   neg.SPI1@32840  3.35317      .    -0.298483\n",
-       "..    ...       ...       ...              ...      ...    ...          ...\n",
-       "69  chr11  47395483  47395492  neg.SPI1@-16916  3.31094      .    -0.567760\n",
-       "39  chr11  47400211  47400221  neg.SPI1@-21645  3.35527      .    -0.900000\n",
-       "37  chr11  47400225  47400235  neg.SPI1@-21659  3.35844      .    -0.729126\n",
-       "68  chr11  47400376  47400382  neg.SPI1@-21806  3.31094      .    -0.329538\n",
-       "58  chr11  47400703  47400709  neg.SPI1@-22133  3.33067      .    -0.325769\n",
+       "    chrom     start       end              name    score strand  attribution\n",
+       "44  chr11  47216350  47216357   pos.SPI1@162219  3.32194      .     0.543324\n",
+       "52  chr11  47257597  47257605   pos.SPI1@120971  3.30576      .     0.680858\n",
+       "63  chr11  47257633  47257637   neg.SPI1@120939  3.32386      .    -0.221565\n",
+       "61  chr11  47257734  47257739   neg.SPI1@120837  3.32558      .    -0.273827\n",
+       "30  chr11  47345731  47345736    neg.SPI1@32840  3.37933      .    -0.296822\n",
+       "..    ...       ...       ...               ...      ...    ...          ...\n",
+       "36  chr11  47400211  47400221   neg.SPI1@-21645  3.36227      .    -0.900207\n",
+       "41  chr11  47400225  47400235   neg.SPI1@-21659  3.35864      .    -0.728760\n",
+       "74  chr11  47400376  47400382   neg.SPI1@-21806  3.31023      .    -0.328370\n",
+       "52  chr11  47400703  47400709   neg.SPI1@-22133  3.34088      .    -0.327795\n",
+       "77  chr11  47523759  47523763  neg.SPI1@-145187  3.30706      .    -0.209094\n",
        "\n",
-       "[135 rows x 7 columns]"
+       "[133 rows x 7 columns]"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5289,27 +5656,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:04.252450Z",
-     "iopub.status.busy": "2025-11-21T06:33:04.252317Z",
-     "iopub.status.idle": "2025-11-21T06:33:05.951332Z",
-     "shell.execute_reply": "2025-11-21T06:33:05.950859Z"
-    }
-   },
+   "execution_count": 49,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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cuHGSpKioKGVnZ8vW1valjRkAAAAArPnvef0ZAAAAAPCr4+LiYlxfvXq12PiSxJSGk5OTRo8erRUrVig2NlZJSUkaM2aMpGdnLc+ePbtc+Q8dOmScBT1v3rwii+eSlJiYWK6+SsNyS/OynkdumSM5ObncY/olOTs7G9cJCQnFxl+5csW4btSoUYWM6b+N5Yso1ra9L22u7Oxs3b17t1z5AAAAAKA0KKADAAAAACpMx44djbOdo6OjlZmZaTU+PDy8QsfTpEkT/fDDD8YW0efPny+wVbnlVtvFnWl+69Yt47q489v37dtXytGWnZubm3G9a9euMuXo37+/cf0qx14Rnp+3LclYJW3N/v37C70XRbNcIV7WYwMKy/Uy8gEAAABAaVBABwAAAABUGDs7Ow0dOlSS9PDhQ/n7+xcZe/78+RIVN8vL1tY238r4nJycfO2Wxbritvu2PE/b2ur569eva9OmTaUdapl16dJFr7/+uiQpLi5O27dvL3UOHx8f4+WHlStX/r9eBdy7d29jFXpERIROnjxZZOzJkycVEREhSWrYsKH69OnzSsb4a5Oamlqq+NDQUOO6Q4cO+dru3r1r7NRQEpYvfTRt2pRt9AEAAAC8UhTQAQAAAAAVau7cuTKZTJKkv/zlL4qJiSkQk5qaKm9vb+Xm5parrzVr1mj79u3KysoqMiY6Olrnzp2T9Gyb8nr16uVrb968uXEdFxdntb9u3boZ11988YXu3btXIObnn3+Wh4fHSzvfvSRMJpOWLl1q/P773/9eYWFhRcbfuXNH0dHR+T5r3LixZs6cKUm6efOmBg8erOvXr1vt9+zZs5o2bVo5Rl4xbG1tNWfOHEnPdhXw8fFRUlJSgbikpCT5+PgYOw/MmTNHVapUeZVD/dXw9PSUp6enDh06ZPXvMisrSx9++KHx0kHlypU1duzYfDH/+Mc/5OrqqrVr1yotLc1qvxEREfmOVpgwYUI5ZgEAAAAApVf5lx4AAAAAAOC/W58+fTRjxgytWbNGjx8/lpubm373u9+pf//+srOzU3x8vDZs2KC0tDR5eXkpODi4zH3FxcVp06ZNqlmzpgYPHqwuXbrIxcVFVapUUWpqqqKiohQaGmqshl2wYEGBHG5ubqpSpYqysrL0+eefS5I6depkrMauVq2asb15r1691KNHD504cUI//fSTXF1d9Yc//EFt27ZVbm6ujh8/ru+//15PnjzR5MmTtXHjxjLPrbRGjhypuXPnasWKFUpPT9eIESPUv39/DRs2TK+99pry8vL0888/Kzo6WgcPHtQf//jHAqut/fz8dPbsWR04cEBxcXFydXXViBEj1K9fPzk7OysvL093797V+fPnFRERoStXrsjGxkbr1q17ZfMsqdmzZ2v37t2KiopSYmKiOnTooClTpqh79+4ymUw6efKkAgIC9PjxY0mSu7u7UXT/X5SXl6eQkBCFhITIyclJ7u7u6tq1qxo2bCh7e3s9ePBA//znPxUcHKzk5GTjvkWLFqlNmzYF8iUkJGj69OmaOXOm+vbtq549e6pFixaqVauWsrKydP36dR06dEhRUVHGPR07dtT8+fNfyXwBAAAA4DkK6AAAAACACrdq1So9efJE3333nXJycrRx48YCxeRZs2Zp5MiR5SqgP1/p/vDhQ23btk3btm0rNM7W1laLFi3S9OnTC7TVrVtXH374oZYuXar09HQtWrQoX3vTpk3zrV4ODAzUwIEDlZiYqLt378rPz69AzhkzZmj27NmvtIAuPVsV7+TkpI8//lhPnz5VVFRUvgKlJcuz35+rXLmydu/erfnz52v16tXKzs7Wjh07tGPHjiL7bNy48Usb/8tkY2OjPXv2aMKECdq5c6fS09P11VdfFRrr6empLVu2yMbG5hWP8tejffv2io2NVW5urlJTUxUUFKSgoKAi42vUqCE/Pz+99957BdqaNGmiOnXqKC0tTdnZ2YqIiDBWrBdl9OjRWr9+vezt7cs9FwAAAAAoDQroAAAAAIAKV6lSJW3YsEGjR4/WN998o+PHj+vBgwdq0KCBunXrpmnTpmnw4MGKjIwsVz/ffPONfHx8FBERodjYWF25ckV37txRTk6OatSooVatWsnd3V2+vr5q1apVkXmWLFmiTp06KSAgQPHx8bp7926R28I3a9ZMZ86c0Zdffqng4GDjLPSGDRuqd+/e8vX1lbu7e6Fbhr8KH3zwgcaPH6/169frwIEDSkhI0IMHD2RnZ6fGjRura9euGjZsmLy8vAq939bWVl988YVmzJghf39/RUREKCEhQWlpaapUqZLq1q2r1q1bq0ePHho8eLDc3Nxe8QxLzsHBQcHBwTp8+LA2bdqko0eP6tatW5IkJycn9e3bV5MmTdKbb775C4/0l7dhwwYtX75cBw8e1NGjR3Xu3Dldu3ZN9+/fV1ZWlhwdHeXk5KQOHTrorbfe0pgxY1SnTp1Ccw0dOlSpqamKjo5WVFSUTp06pStXriglJUUZGRmys7NTrVq11KZNG/Xs2VPe3t7q1KnTK54xAAAAADxjMj8/2AsAAAAAAAAAAAAAgP9hBfdnAwAAAAAAAAAAAADgfxAFdAAAAAAAAAAAAAAARAEdAAAAAAAAAAAAAABJFNABAAAAAAAAAAAAAJBEAR0AAAAAAAAAAAAAAEkU0AEAAAAAAAAAAAAAkEQBHQAAAAAAAAAAAAAASRTQAQAAAAAAAAAAAACQRAEdAAAAAAAAAAAAAABJFNABAAAAAAAAAAAAAJBEAR0AAAAAAAAAAAAAAEkU0AEAAAAAAAAAAAAAkEQBHQAAAAAAAAAAAAAASRTQAQAAAAAAAAAAAACQRAEdAAAAAAAAAAAAAABJFNABAAAAAAAAAAAAAJBEAR0AAAAAAAAAAAAAAEkU0AEAAAAAAAAAAAAAkEQBHQAAAAAAAAAAAAAASRTQAQAAAAAAAAAAAACQRAEdAAAAAAAAAAAAAABJFNABAAAAAAAAAAAAAJBEAR0AAAAAAAAAAAAAAEkU0AEAAAAAAAAAAAAAkEQBHQAAAAAAAAAAAAAASdL/ARWHfTskqdRJAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x14c0bb7a2110>"
+      ]
      },
+     "execution_count": 49,
      "metadata": {
       "image/png": {
        "height": 200,
        "width": 1000
       }
      },
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -5318,19 +5682,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:05.952850Z",
-     "iopub.status.busy": "2025-11-21T06:33:05.952701Z",
-     "iopub.status.idle": "2025-11-21T06:33:06.731536Z",
-     "shell.execute_reply": "2025-11-21T06:33:06.731101Z"
-    }
-   },
+   "execution_count": 50,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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NKXW0sOTEDQMG3u46hD9LjleR489drRUip6iuCEkRSegT28e1TIQOIXL8WnKaioEtVwMrBwMbLgZK1ga9bwRBhBckcgiiMxEB2AqV7+3agNYtgRVZcsILIWgSDAnQq/WI0ERIMTm+3JoYk5ILJE5oeWBFO3/iG5xWHH0yoPNcsfOrr4A1a1q2MwZA69xHuGnGDQcG3+naRmRVa7Ulx1/igVamkE6NTkVqVKprmQgdIj7LPcawBTUHgVXDgYLPeRHlklXAhguB8o2d0sdOwVzBLf22wF0vCYKQQyKHCFvMZuD554Hhw7mLzA03ACdPhrpXfhCB2PaOVyExMVJcThM9F8MKd0uOmLbakhMztOM7ZuHZzKCJ9brJa6/xqVIJvPgiUFoKfPABEBsLSeR4ycbWowef1tZ6PrY/kdPk5QUw0Jgck82ESmMlUqJSkBKVAgCUfCDEiKQpPn/Ddt0q3ZvuWLzcSOGErQnYcx+wIgX4YRiwPBHYcw9gp0BLgmgtlF2NCEvq6oBzzwX275fa8vKAffuAnTtD1y+/uEROx6sQpZK/NJaWygvqEV0fIWhKGkqwvXA7tCpt4BaFAIRImxGxNOoo57Id2Ci5wNnHf4Tt27lS+ctfpIr0f/oTMHQoeLppAGg67fHwyc7cCKdOeT59oJacv2X/DbH6WDDG8PSWpwN2Vyup5+IrNSoVqdFkyekKCIt0Y6OXDepyJYtNr7nAiCeBpgJg7+JO6V/Q2fEX4NSn0rLdBOQ+DwxYBET1DV2/CCIMIZFDhCUPPSQJnKQkXjRuzx7AFkB9wpDiilFwvoQZevG4ieIfOuTwyclc5Bw/7ns7s80MO7NDrVRDq/JcqJHoPIQl5+41d8vam6xNiND4CU4Q8S6aGD7NeQ44+m8+n34lMPrZtndMxJCp3aLASyXftKO5NteI+5VXyncdOxZAsW+R07Mnn+bnc/e25qmkA43JefS8R5EcxRXTc9ueC9hdTVhtUqMkdzWy5IQWkXDAq8g5s5VPI3oDU74EVFoe69XjXMBU2il9DBpVuyWBE9Uf6PN/QM0+7o5HEESrIXc1IuwwmYA3nWVBpk8Hjh0Dtm3jo8G/+13gx/npxE9YsHwB/vrdX4PST4+ItL6WGj5NmgiMb1/CAXdSuMcNTpzwvd2ktych8qlI3PTdTR12bqLtuOJvmrcH4rImMvapnALaWstHtpsKAHMrCop6wpUC2nNmgD37uUBWKICpUz1soHWKHFOZx2MIS05TE3DmTMvd/bqrOd3S9Gopi5xerQ/YXU0kGUiNTkVSRBKUCiUlHggxvXrxaak3vVKz17nhXC5wBJpoIHpAMLsWfAq/5tOYwcDsvcCoZcD0H3hKeDFARhBEwJAlh+jS5OcDK1cC9fU8Le3s2dyCI2opPPMMEBfH55OTeYxOoHx+8HN8cuATAMDD0x52ZVcKKoY0Pm3wY2oJEJuNx+GIEXDx0nj0aMttS0u5CGq0NGJ/GTeDbSvc1upzNloa8dzW5wAA142+Dumx6W3qOyHhTcxUGiv935fiRc/mbei7HYgXK5tnsVFaxsfJ4uK8FKAV7moAUL2Xi3o3xP0KAIcPS5YdADhwQBIzcwfNRWZsJgDgw/0ftnBXM2gMrv0MakPA7mrCNS01KhUqpQrJkcme3dXsJl7IVx0N6HsEdGyibfTuzafFxTxWK7a5F6aoCeUp0Ua44LACxau4hVPfA+g5HdD3BCp38PVZf5FbT3vPDUk3CSLcIZFDdEkaG4Hrrwe+/FKeVCohAbjnHj6fmgqMGyffz19xeHe2nN4im7869up29DhAhMip96BCAqSuDvjXv4D33+dxSDodMGkSF3zipbGoiLus9evHlw8c4EkZLr0U+K34N9iZHSqFCkcrj6KyqRKJEYlez9ecLae3YOnGpQCAxIhE3Dr+1jb/LQRH1MkJtF2G0mnFsDoLZ6ojueuaSP3cHoQLnM15bIUSmPoVsPc+oOE4mpq4yEnwnHhNLnIKV7QQOe6iZt06bpkFuGXno48A03QuZu6ceCemZ/KVvxT8gj2le2B32GG0GaFUKKFRSvnTDRoDak2BBaAL17T3972PdfnrYLQZUdFUAQdzQKlQ8mu6/yHgxHvSNYjqD2S/CqRe6P3ARJsRIgcAcnOBCc21jKWaT3VJfFq2HqjYzufjRwNpFwW9j+2i+Aced+OejEOhBs5fD1Tv4svJ00PSNYLobpC7GtElufVW4IsvALUaWLSIv9A//DCvnXDa6d6fnt46UeNOlbEKORU5GJs6FgCwpWCLnz3kFBfzuiAzZgAjRwIXXAAsWxZA6mYD9/uHqYynCPWGtQEo+B9w5F/AifeB2sMAuPg77zzgkUe4S9rIkfyzZQvw88+SuxrAhZBg6VJpfttpbr2ZN3geAGB74fYA/2rOz/k/e5wn2k673NXEPWVxbjt0MTD5s47pWGQmnxpLAJuRf+H6XObKmqZW8xEIr3WZNHHc+gEAx14FTOVA3RHX6rQ0adP//Aeodr6/Pv88UFkpWXIiNdKodqSWzzdZm9BkbYJBbYDC7YegNe5qwmrz/bHv8d9d/0WNqQZWh5Vfd+YAfvm9M77JAaTNAdIuAczl0og7gJM1J/HS9pfw0vaXYKEMWO3GXeTs2iVft2sXpBg0kQyjZDWw/0H+KfpG2tjawAXFyU+B0p+lQQB/KBTeP+2l8RSw+Qr+feoxDRj1HDDsUSAqCzBXS+6lwq35+NvAnnv550zrnlEEQZAlh+iCVFUBH3/M5597DrjtNmndPfcAdzrLbMTEtP0cW0/z4NUFwxcgvyZfZtXxx7Zt3G2utpaPYA8fDpSXAw8+CFxxBU+C4BWD21td4bdAv+tbbpP7Ih89bp5mesqXeHP5/2HvXm69WbuWZ5gDuCvayZPcyiN49VVgyhSekGH5cuDaa51/e+FWKKDAzWNvxvKc5dh6eivmDJwT8N//c/7PiNfHY0DiAGw4uQF2hx0qZcfX/Tmb8OWu5pco5w1XvbfjOiQwpHCXNYcZqD0AJI6XnzqSi5wqD9l8AfAXw9ghXBTYGoGVg2SZBXv1AuLjubipqgL+/Gf+fXrmGeC664AGq1PkaN1EjlPwNFgaYLQaZfE4AHdXszqsAd2X3pIMFNUXoYfxBFC2jlvGZu3kcRIAf1lukILe3t79Np745QkAwJCkIZjVf5bPcxK+ETE5APDhh8DNN/PMkbW1wLPPAp8tFJZLL5ZK5uCZ1o69Iv8NVemBOUeByE5wS/bGqU/5/d9jGvC7nwFxf57ziNNC5XRbUDndL09/JSUdiMwEekzp7B4TRFhDlhyiy7F6NWC18vejG2+Ur4uNlSphe32xCoDNBZsBABN7T8T4XuOxr2wf6s3+R/oYA265hT9wL7iAC4sNG3jq6sOHgUR/Xl/aBCnV7/4HAdMZoLFAWl++CdhzFy8WOukT4LIKYF4hMP5tQN8TnzoT7yxcKAkcgFtwJk4ExoyR2ux24KqrgKefdu8/w/bC7RicNBjnZpwLtVLdqricGlMNdpXswtT0qZiWPg3VpmrsK9sX8P5ES8w2MxqtjRiQMAArrlqBFVetwOIpPB1uQJacaKdPYuWvHd85hVKy5pT/0mL14EE86YHRKFlYWxAzRJq31kgZ28C/46NHS6tXruQWUYezZq6w5ERpo1zbCMHTYGmA0WaUxeMAUhICo83Iv7CnlwPbFgKrxwKrRgMbLwXy/gsAXpMMFNUVSVaBjAWSwAF4gHv8SNfiVzlfIdGQ6Jon2kdEBJCRwee3b+eCt6QEWLCAW9Chd/o4mp2ZKvpeC5zzsHSAY/8Bcp/lv7VT/wdcegK48Fdg4O2BWWN8Fd5tL8VOwdLvL5LAAQClmsfmKJ3xdSItvMogtREE0WpI5BBdDpFVp2dPqSK6O8JSkp/f9pTRW05vgUapwejU0ZjQawIczBGQ29b+/VzQAFw8REdL64YM4aPSPlEopNFwUxnwbQbwk1taquNv8+ngu4HMa3hMQ0QvbvHpOQ05OXz1qFGeD5+UBPT1UUohryoPFU0VGNdrHPRqPYb3HI4dRTtgcwR2ITed2gQHc+Dc9HNxbgZXWeSy1j6EtSYrPgvzB8/H/MHzcWG/C2XrfBLlFDl1OUDZho7vYLTzC3f0JSmOgPFy9KNHWF2brV4t300IFSSM9Xn48eO9rxMDD+7uakLw1FvqYbQaYVDLfySE6DFajcDOW4DNlwNVvwEZ1wCDbudxG+WbwBjzWhOnuL4YaDzJF6IH8mnJWmDddP7ZuQgAkHMmBzkVObh62NUYlTIKX+d+DbvD7vPvJfwzbZo0/+CD3K3x+++dDXFOgSnct2KHAAnZ0g55r/Np9qtAn8t5bZnE8cCop3na6VBiLufTCKc16eBjvOjnihRufRJW2fo8Pj33K27lIQiiTZDIIbocQjhUVXFrRHPEA7Cqigcru+M1NsANs82M34p+Q6w+Fv/69V/Iq+IPlEBc1krcYkUzM/2fyyNJbi4HdiPA3ARGfS6fxo/i07w3+Qj06rHA/kdchfJ8xf7M8eF5Jqw2h8oP4cZvb0SNqQaN1kYcLD8YUNeFoBnXa5wrnolETgD48PMX1poEgxS9L+YDEjnxY6RUz5svA468zEezO4oezi9cUyGwdhLw80ygejcAIDHR4Rp1//e/eRZEgA8+3C1K/vSc7vPwvu5XV0yOB3c1ET/jyV0NACw1B7nFRhMHXLAFGHIPkHUtMOIxYPLHqDHVwGgzYmLviSi8sxCFdxbinbnvAHC6sblqWjnd62z1PDaufCNQvQcAsDxnOQDg0oGX4pIBl+BM0xmXlZhoO+ef72Ol+G0s+pbH3bjDGFB7iM+LwaS99wM/TeOf08vb1zFf8TqBWIlExjSRPEETx2OLTGXc/U702T22iCCINtMpIufVV19FZmYm9Ho9JkyYgB07dvjfiThrueACPrVagR+a1chkjAfaC4vJ7bdzNzHGgM2bgT/+0f/xd5fshtluRkVTBe7/6X58fIAHAAUictyFjbCqtJo+l3lfJzIGGZ3mrMgM/qneDTSedLmo/fij90PMm+d9nYhF2lWyC2/teQv5Nfmydn+sP7keADDj/Rno/SIfFf2l4BdY7VZfuxE+XGCEkPEocgJxV9PGAskz+LylGth9B1C8sqN6DvS6VJpvPMXjVNwQ99uBA9wqc889fPq20yiJ2HOk5AjupMwEwN0se3jJytxgaYBKoYJO1ACCJHKqjNylx5u7GnMm60D0AEAbz9P2ilHzFSkoruAm2YzYDPSK6YVeMb0wtMdQAE5LjsgEV8bvefS5jLs9ubE8dzmUCiVMNpPLwiSED9F2fv97z1Z8AECPqQAU3F1t/flA4TdAsdPMo1C4kmLAJKwm6TwF+JlfeBrw9uDJdc29jTFu8dtzD7D5SmDrH4Ht13J3ybL1vGApAJQ6f8AH3QaMcivWmzSZT0+8CxR8yeOLLO3wyyaIs5ygi5zPP/8cd911F5YsWYLdu3dj5MiRmDVrFsrLy4N9aiJMycyUUsnedBNPQnDsGBc8F13E68KINNJHjgDDhnHrz7nn8pTK/hBiZlrGNFwz7BpcM+waJBoSsb1wu1+3rcGDeTA/wBMgFDufmYwBP/3kuaBhC2KHea7x0HchkH6V8w97ibsGpV4IDPiba5PrruPTNWuAxYt5TFBJCfDOO8AHH/B1558PTJ4sP3RMDE8zva1wGzRKDRaNW4RF4xZh4ciFAAKrl3Om8Qz2l+1HlDYK/eL7oV98P8Tr49FgacDO4p0B/OGEJ9ptyQGAgX/33K6J8tzeGmKHAMkequyqIgB1JO68U3ohzc3lmdH27HHbTqHk8RDuqKOAATz1uFoNPPRQy8PPm8dFTqQ2UpY9TVh1qo18NNybu1qNoS+PbavL4d8lhRqY/iMvUGoqQ1GdVCNHkBrN54vqi4Be83g8RPl6YP/DQEM+L7DqJL86H7tLdsPBHJj/+XwsXsfjqJbnLoeDOdBhdFRmrzAiJqZlPCbAywYgMgPoPZ83VO4AfpkPHH9T2ij9Sj49+A8ubgYukrYPNjnPABsu5IMB498Exr7E7/P+f+XxNX2u4NsdfwvIeZZnGmxwe2hlXM1FmsMCbLkS+FwDHHmxc/pOEN2QoIucF154ATfeeCOuu+46DB06FK+//joiIiLwzjvvBPvURBjz7rs89qa0lFtnBg7kbi0/Oz2j7ruPZzIDuMBodNZB9Bv4DynpwNtz38Ynl3+CTy7/BHMGzkGDpcFVJNMXb74J9OkD/PYbT2Pdvz8fib7gAp6QwC8KBTDudf4CJki7BBiwCEi/GkiZBdQfAb7rB/w4DtghPe0vvRS46y4+/8wzPP4mLQ244QZJcCkUwMsvy0/5z38Cuuh6HCw/iFEpo/DKxa/glYtfwdtz30akJtKVVtoXG05uAADcO/le5N2Wh7zb8vDanNcAkMtaexC1cNxFToQmAlqVNrA6OQC/f5oLifjRwPB/dEwnJ7zLR8QFSi0w6SNAl4DMTJ7iPaqZnho+3G2h/81Swg0AGPMiYJAqgd5yCzDArVj93LnAJZc4RY5bPA4gWXKqTU6R08ySI0RPrSoWGPogL2T6YzYfXS/6zpW6vbixDIAkbAAgJYrnYC+uL+aZ5cb+m6849CTwXRaPxwEApQ4rclcAAJaetxS/XPcLfrnuF1w+5HIU1hWS6O8Ali3jv/uCHj347xgAYMxL3ELnjjqSC9NzHuEpmQtXAN+kAz+dy104AR5LlvcmsOs2IP9DoGQNz2BW8AVw6jNu7WsPwm2U2fnHUgOU/QT8NAXYtxjoMZnHWzI7rzX1/WA+BbgI0kQDE96RsqsJsayJARLGtDgdcfZgtplRZaxClbGqYwdROgEHc8Bit8Bit4B1RAKPVhDUFNIWiwW7du3CAw884GpTKpWYOXMmtm1r+VJlNpthFqXsneh0Oug8ltLuYMyVQM0BPh/VF4jMQGFdIQ6WH8SQpCHIiMtwbSrS5p6f5eY43HiKBwtqYoG4YYBKj9JSXstEqeS5//V6Pu9w8NHLOkUB1uevR35NPvrG9UVxfTHUSjWy07Ixqc8k2M16nDrF06vqdIBWy19gHQ5u7XCvBN3QwAPis7P5tidrTiK3IhfDeg5D7xgp2HLTqU0w2UyuwGZBaSmP8/AWtH7yJB+tda9QHjCNBYC1Fogb7nH1kSO8eGVmJpCVxdsyM4GDB4FvvgG+/pr7+mdlAddcw9er1byOzpo1wHvv8VicadPko3+Vlfz619fzaxYXx4tjRuuiMX/wfPSL7+fadt6geShrKENZQ5l0AGsD9713WIGINCB6EKBQYMgQ7iL33XfAqlU8NqhHD25ByZBuE9jtwO7dUt9lMTzxo4Bp3/ERvcQJPCBa6fw6nreS1344/RV3yYgZAgy5F0i/CgoFHym/7jpuucnN5dmIJk3iGdcE2dn82vz3v8DFF/PtD58pwAVZF2BWPynFrUqpwg2jb8CRyiNosjYhQuNMXWcq577tKgN3N9JEo9pUjVn9ZmF2/9mu/adlTMOsfrNgtrt9b611QP1xXrdFHcVfOPQ9sfb4WujUOkzLkKKKi+qKcKD8AAYnDUZmnHSBGOMicuRIfj93NKWlgMXCRaonjhzh3y/3ukMBU3OAv1jHjwVU/jMjxRviMavfLJerFAAoFAr8fvDvYWcBBrErFFw4RPQBKrbye2rgInnVdA80WZuw6dQmJEcmY3SqlOYs50wOTtWewri0cbxQbGQ68LufgIOPc4EzYBGQIG1/xRXA2LE8GceZM9wS+9e/up1IGwv8bh1w+Bk+qp75B1k/tFo+gPHII/y7tGQJ/5Ompk9FcpT8R2dw0mDM6jcLiYZEzOo3CxN7yQuMjk4ZjVn9ZnGLz4jHgLSLgcKvgbrDABRAxlVAj2mIqLViVr9ZGJ0i/R1alRaXD7kcWvF/638TdzE6/gb/fdfG8+WMq1G56WnM6jcLfxv3N/SI5P52NocNDZYG5FXlYXwvHxkV2kleVR7yqvIwKmWUS5gBwPr89WBg+F1fD5Y3TzAG1Ozjwk/XA4gdCrgVVgWAsjLumhsTw7+PTGHDz/k/I0YXg4m9pWu/q3gXKpoqMDV9KkoaSnCs8hhMNhOSo5JRUl+CeEM8hiQNQaOlEXnVeRiZPFImMNedWAeVUuUq+hoRwdPkP/UUYDbzBASu39fIdGDWbuDQ49waEjsU6H+LlB76or3A6f/x31FbI9B7HtBzBq91dOoTp3hQcCujuYL/zlvr+cBAzKBW/jfcGHw3j6spWc2FlEIBQMmzqYnYtNHPAX3+DzjxNndLNqQCqRdxgQbwvs4+ABxexn9D40cD/f7KBwXMlfxdw2EGVJE8Q5vFObIWN8JlubXZuDW1tpb/hg0dyt89XFhqgZq9gCaeP5ebWwqNJUBdLi950JbrYa7gx3A+8+vN9dhyegvSotMwInmEa7ND5Ydwuu40JvaeiDh9XODHr9rFn8dttFQfOMDfZXr25C/ga46vQYwuBpP7SC4Qp2pOIacip8U7VHk5f+4OGOC0LHo49pkz0jO/sZFnnmxq4omBxPNMXHKDgb9fNTbyQVqdjq9jjM+b9QX47sh3OFp5FCOSR6C8sRyVxkpM7jMZF2RdgJ3FO1t853MrcnGy5iTGpY5Foq2CuzHre/Lnud3IvxPMzp/tKh1/x6nL5f83xrjbfHR/QO18H7BbgIZjgLGMv6dE9AYi+/KO2s1Aw3G+r93EB281MUBEH5y2WLE6bzWOVB7BgIQBKG8sh8lmwsTeEzGj7wxZ1sziYv7cjYsDRozgHjsdgYIFUVYVFxejV69e2Lp1KyZNmuRqv++++7Bx40b8+qvcv3np0qX4xz/kI49jx45FmrNi3LfffQcAmHvppfBEW9bH6U24dfx+jE0th0opXYqS+ggs2jQaK/pvQXJtMkac4l9Mo9aIzUM2I7U6FdknstEjogm3jt+HkSkVUDpvWpNNhZVH++KDfUPAGOBwqMGYEtxwxgAwKJV2KJXeX2AYU+DEibloaOiF2NgTiIs7BqXS5loXEVEOjYYHxDY2JmPr1qdgMvVAdPRJTJ78IAp778ehPocw/NRwZFZkuo7707CfYFFbcPHei11tTU09sWXLMzCbYzFx4qNISpIHoTc19cCmTS9CrTZi2rS7oNXKUy37uu4jk8/g4Wk7oFM78HN+b7y0fRQAfqFqa/vi8OHrUVEhpWNNSDiI8eOfcP1tbaG8fDQOHrwRFksMsrK+QXR0AZRKK6zWGKhUJqSm+s6iZlDbcMu4/ZjcpwRalQMOBigVQI1Jiyc2jcfRSt8p1BgDTpyYjxMn5sJkSnK1x8cfxtSpi9v8d3UGERorbs4+gCl9iqFR8e+D1a7EltOpeGGb75HESI0Vf80+gCl9SlBUH4mC2mgkRRgxtEc1vj3SF/9nL4SSKXHhfklg5/fIx8H0gy3u092770JR0XTEx+dg0qSHoFJJboTt/R0wGpOwefM/YbHEYMqUxYiLk/s4FhWdiz177oRKZcHkyQ8iNvaEbL2346sUDvw1+wAu6s9dmmpMWvzntxHYXujhSdhFMGlMWDtiLZLqkjDpmPQbvTdjL04nncbU3KmIb/SXMpDwh797srXkJechp3cORp4cifRKSan/MOoHaG1azDw40++5p6YXYeHIHCRHSVlMGi1qvLd3CH48ngmLJRqHD1+LoqLpcDi48NHrz2Ds2GdRN3A1DqQfQI+6HkhoTECtoRZlcWUYWDwQA0sHwheF8YXYk7UHg4oGuba1qCz4cdSPSKhPwJSjVAvGE2qlA3MG5CMxwoRfC5NRb9EiJaoJ0VoLtCoHNp7qhXqTAbm5f8Lp0+fDapUKyel0VZgx4xZoNEbMyDyNG8YcQoyOW61KGyLw1u5zsKMoBQowXD3sKC4fmgetilsLTlTH4PWdw5FbIVmafd3PiQYj/nnBZvSINOGb3L54e88w1OvrseGcDUipTsG4E+Nc2+7J3IPCxEKcm3Mu4pri/B4bAK485yj+OOIIKpv0eHDdZJQ0yAdy/O1/4sSlOHToBqjVTZg06WHExh3HyrErEWWMwozDM1zbHUs+htzeuRiVPwp9qrh4Liqahn37FsFuN0ChsKNfv68wePBHUCiAM2dGICdnIWprJQtjYuJ+TJiwVPb8ao7RmICGht4AlDAYyqBS8f8LY4BKZYNOV+N1XwAojivG7qzdSGhIQHxDPExaE0riSpBcm4wx+WOggC9XV4brRx/GBVkFOF4diwNlSbDYlbhsyHFEaa34w1ezcNmQ45g94CSMVjWOVMaDMQXOyyxCQW0U1hzPwJ9G5GBbYSr+d7g/IjVWjEk9g6npxcipSMC/fh3ls+/i7z98+HqUlEwBY1zZ6PVnkJ39NOLjjwGQ/qeA9H/99ttv/R6b/4lBpKioiAFgW7dulbXfe++9bPz48S22N5lMrLa2VvYxmUzSBiK0zxv+1nvip+mMfQLGtv6ZsfoTjFkbGVszibfVHGYzP5jJsBQtPmvy1jBmtzK2cjDfdnU2Y7kvMXbwKb78/bDW9aMdXHqpPPLx6qsZe/231xmWgr247UXZtj2f7ckSnkmQtd1wg7Tv8OGMORzy419zjbR+yRIPHfB23e02xr4/h18P8Tm0jDHG2MmTjMXHS7sqldL80aNtvxaMMXbVVfw4F1/MmNXahgNs/TPv68Z5jDWc5Bdk27W87fTXfndfskT6W6ZMYezvf2dszhzGIiLa0JfOZsMl/O/8Op2xA48xlvM8Y5/pGPtc73/fHTfzfXfdxZdrcxnbv9T1/cp6OYvpHtfJdnl84+MMS8E+PfCpq23jRvn9/Oyzzc7Tzt+BG2+UNpk2Tb7OYmEsJUVa7+Fnyvvxxd/v/vlUyVjFDu99DTEmq4lhKdjo10fL2ud/Np9hKdjRinZ+GQlOW55NPnhz15sMS8Fe2PqCq81iszAsBRv52kj/5674ld+bn+sZO/xPvnzwSX7P/noTs9kYmzBB2tVgYEyn4/Mvv8wP8cqvrzDVP1TswZ8eZLrHdWzp+qUB9f1oxVGGpWCXfX6Zq+2n4z8xLAW7a/VdbbkchJPrruP/o4QExl56ibFNmxhbvpw/EysqGGNF37f8jfoEjH3Vgx/g4BOe1/80XX4iX/fz7nvk+xZ9zwpqChiWgs36cJZs099/9nuGpWC5Z3IDO7a5irHPDdKx157bchsf+1dU8Oew2GTUKP541z2uY5kvZcq2feTnRxiWgn156EvGGGNvv908wwT/bN3KPxqN9C6TkSF9X6qqPP8pHclbu95iyn8o2fNbn2e9X+jNZn80m1lsFv87nv5Gen9ljDFrA2O/XM7Y8hTGvohiLO8tvn7NZP6+ayxn7MQHvG1FGmMNBfx3Y8ctjB19lbHj7zF24n3Gjr/DWPUBv6dvamJs8GDpWqakMJaYyOfffNNtQ/cL3kqC6q6WlJQElUqFsrIyWXtZWRlSPPiBtNs1rbVGqbpcoHwDd0GY8BY30x9+mqdzdHLz2Jvx04mf8MDUB3Bz9s0Y+8ZYxOpiMTNrJlC6hh8jsi8wczM3+5kreJHHTuLnn7nblDuffQZMuIn79Jps8pzKJpsJ0VqpuIvZDHzlVr/uwAFg/Xrgd07Lp93OXbIE774ruZL4peALKZ2n4NCTQNb1ePzxnqiu5i4J77wDXHkld1l77LH2x9i+/z5PULB1K/CHP3BzvVrN/5aRI4Hrr/exs7UeKHBW3JzwFjfbHn4GqArMx95o5O5kAHDHHcCLbjGjublt+nM6j8ZTPCuXygBcsJXX5wG4i5IjgNzctU4LYJIzqUJTITeND7kfSBiL+AOHcKL6BMw2M3Rq/j2vMdUAAOL1krXg1Vflh33pJR6HJHO3aCM2G7BihbS8aROvfTTC6UHx1VdSnSYA2LGDb+Net8Mjjaflwc8C5pCKFnZBdGodorRRrkxlArGcFJHkaTcixIjvi/j+uM/HGwKwvB17jd+bQ+7jrrDmSkAnZYv4/HNAOFo89hhw//38d/mzz6RDLBq/CAMSB6C4vhhfXPEF5g6aG1Df+yf0R7w+HityVkDzOLcQMeeze1yvcb52JXxQWAh8+CGff+cdeZbN3//eObPaWXMnIh0Y+RSPIdrq9AG3NvBnHcBd64YvBWpzeK2pQLEZgRPN4q33PYio6esBSCnhBZ6K/frkxHv8mSI48wtQvBpIuyig3V99lbuNCfbu5W7dkdpINFnl3iNiOVITCaORfwc8YbcDjz7Ks8FmZgLffsvjERsaeAxZRzy3/HHDmBugV+uxr2wfrht1HR6Y+gA0Ko3/HeNH8UQs9ceAou95gpmp/+NFypsKpayFMYO5m5rDDBiLgJHLAHU0dw89p+3vu2+/zd+LlEr+bvmnP/H2lSs7LtdKUEWOVqvF2LFjsW7dOsyfPx8A4HA4sG7dOtx6663BPHVgiMJ2+hTJD7liKxA1gH/UUZg3eB5So1Kx8uhKnJdxHiqaKnDv5Ht5tp/StXyf3vO5wKnYLgURdhIffeRlhY2nUW0ucsw2M3pESPlaf/gBqKmR7/rll5LI2bZNvr6ggP8wuFcp90qhh1SqtgawM9vw/ff8F/i224CrnAnFevcG3nij/YWldTrg2mv5p9VY67l/qkLNaxgAPNNN0mT+EdXfvbB6Nf9xUyi4GHRn8GDP+3QZStbwafLvuMBpKuJ+5fZGeZIEbwy5D6jYBux7CFBoeGxa3z9z8RM9yOVzXWOqccVZ1Jq4P7lYV1YmFyEAF79btsCVPrs9/PwzUNEsln/5cknkvPZay32++CIAkVPwGfdxViiBkU/zIoS//oVn5uriJBoSPYoclULVOj95otMQQsajyNEHIHKcdY5cNbtynuWZwZysdGYgnziRx0kJxEuIoHlsZyAoFApkp2Vj7Ym1LbJZBjOOqbuzfTsfxFEogFmzPGxgOiP9389bKcXIbnPGx1Vu57WgtAnAed/zeAx9K4Nwi79vmfK6Zh+i63hCn+Yip9HKMwZF66IREPkftGzLey0gkcOY59/31auBiPQI2XcJcBYTBk8Cs2KF9Nzo2xd48kmeyfXpp3nM7S+/8HVLl0oJV6Ki+ABBZ7FgxAIswILW7RSZDlx8CCh0Jt/I+w/gsAPaOKDnecCAm3nsWP4HPA6nx7k8hXvDCR6b2U6Ex9mllwJ//rPU3kFevQCCLHIA4K677sLChQuRnZ2N8ePH46WXXkJjYyOuE7lw/eFJzrm3teeNOG4EoNLzFI4N+TzhwDS5n58awPWjr8eTvzyJe9feC61Ki+tHO00BNucXVhvn7IuNK93E8VLl4iDicLS04gh0Ki5yzDYpIJwxBrPdLCue97//tdx3u1vISvM6NQBPlexX5DCHJAKbcehIlGu0/P/+r+X6kGZLjUjjwXi1h3jgasbVwDDnU95u4veLD/Lz+TQ5mQfQhRXWGj7VOlPU2U1A9S4efKr0/XcD4PVUZu8H8t/no3nmCn69IvsCQ4a7XsxqzbWSyDFzkSPW/fwzHxFrTl1du/4yF57u93Xr+MOpsZGLqeY0NLRsa0Hlb3ya8Qc+Mg7w35NwEDkRiThVewpWu9U1+ldlrEK8IV6WupnoOohMfCLDHCCJHPcsfV5RO18qRVHKgbdyYQ4A+p5Y77xtXRaADmZc2jisPbEWd0+6G6NSRuGO1XcAALLis4JzwrOACGeMuMg2qm/+k13pfLDHjfCcBOiM88ev1zwp4Ly1nPFcCFdddxg6la59lhxrPVDrIftpZWB1Fw8dklvpBXY7t9aU1JfI2ptsTkuONtL1fTAYgI0beXZVgA+87dnDxSXAk/yEHYZkXqbCrVSFjIsP88K7FduA487iZxG9pEQZ7WDTJj6dPdv3du0h6CLnqquuwpkzZ/Doo4+itLQUo0aNwurVq5EcaJquYKab0yUCA+8Acp4G1k1zZjBJ46KnajcwYzUA4KaxN2HZ5mU4UH4A1wy7RnLhiHT+IIuicz2mtigWF0xyclqOSgu0ypaWHJEBy13keKrL6nDLTrhmTcv1+/YF0Ln6YzyjmgdKz0jn95bdKqSMeRnYNBfYtgA4+THPDmYs4alAf1/WIvuQO+LBUl/vdZOuS4Tzl7vhOJ9G9wPmnmzdMWKHAqOe8bgqThcHwPPos7AYeEi62KG4C3iByfkV2bGDP/DaRJ3zN6BXBw5BdRKJBi5qq4xVLvFZZaxCRmyGr92IEOLTXS0QS06fy4CKLcDRf/F7NqI3/1jrAVsTRBm73r19H6atCLe0lKgUzBs0D39e8ec2WYUIiRkzgIQEnu3z5ZflVoTGRkBfdRgqgNdpA3gWyK3XSGmq64/yqcimlv8BsOOm1nWi0vsPeLQuuqUlx9KICE0ElIoAfLoqd0h9lRHYO6J4ofZEhCYCdmaHxW5xZVYU7moRmggccCbevegiSeAAPItkURGfV6m8FzUOa9QGnpEy46oOPazRyMMlgDZmMQ2QTvAWBG699VacOnUKZrMZv/76KyZM8FAIMVSMWgZM/pSnacz7D89lX7qG57N3kh6bjt037cb2G7bjpYtekvZNd5ohTn/FqxwLmoqAsg1B7/pOtzARtRq4915eGBMA9KqWMTnCqiPqStTXA8ePez8+YzxVcnMcnn5nmlO9V5qPGwlM/IALSAAKhfSj1OaXymCScj5wyRFg1PM8PqXhBE+BO/p5qQ6CF4RbkzerQJcm7WJAqeMvP8VugVgOm2SpaAdCyAgXNUCy5HSGyLFafcdFtfncDhsX9YDk/uOwB3eApgMRgzaDXhmEhGcSkPBMAkw2E8XjdGGE5fNM0xlX6vuC2gLZOp/0u5H/LldsBVYOAH75PbB+FvB1KlC5HRrnOE5Tk+/DtBXhlrandA/2le0DAyNXtXZiMPDaQgoF8Pjj3OX8nnu423ZGBmBqdAoMndNSb2uSx8zanf9srfP+cTg9UwKFMakMhweitFEu9zRBg6Uh8Hgc93eKNvCrj/FnUWDYPS7HJXLUkTjkvEzCjd8dYUGz2zvO4+BsQJRUAXiZlGARdEtOWJBxNf/4YGTKyJaNUVnA4HuA3OeADbP4slLLCzkOuhtInh6c/jrZu1eav+02HuT24IM8J75wVzPZJZEjBI+w5Ozb5/s97NQprrbbRF2Oc0YBjPsvD0av2AbkvYY+adJBT54MropvMxG9gcF3ALijVbsNGwaMGsX/N9dey4Ppxo7l9Xr+/W/g9dc7vKcdhyYGGHIPTw6x6RIeg6RNACp/5fPneoixagXi5ev6b693Pdjyq/NdhS8Zg+thEgxycjy7wgl8ntuX22xTMY/bAiQf9lXDeFKSMEBYcoTgdLVHBFBZlwgJ0dpoqBQqbC/cjpTn5T+gAVlyNNE8ucjJj4Cib3gtM20cMPA2IHEiBgzgdcqClSwlLToNadFp2F2yG7tLeJzIuDRKOtBebrqJx36+/TZ3K9+xg7tOX3YZoNU5X/dE4H5EL+Cch/i8OgqodrpoCA+MlPN5EDoA6Hr6P3nTaXlSgGZEa6NRUFsAxpjLDbbB0iCr8+SThmOBbecFTwO2AlEfrsna5BpwEzE5NeVRLpflIUNa7uvuuv/bb7xeHuEfhYK/K+3ezQVom2KoA6BTLDndmtHP8qKOvedzNyaVHsi6Huj7J7+7tpfCQj5Vq4GHH+bzcXE804fWQ0xOc5GTkwOfHDnSjs6JDHWJ46RsW04GZjW63NQ+/li+G2O+X0TDgfff52brvDzusxsRwcXPu++GumcBMOIJYMJ7QEI2F6Ulq7ngS7+y3YcWD4/CukLkVuQityIXZrvZ1V5U1A5RHQAHD/pef6ytz1DxYFdFSAVdwwhvYkaIH6LroVAovCaFCMiSA/C4i/438SDzi3bxYq0jnwIMybjIGcf94YctY9I8xTW0hXFp43C08ih+KeBR25RZrWOYNo0/g4qK+P/u+HGe0EcjiuqKhEsRvfnv/YgngKGLgUine2rjST6NzOBxWn0uB3oGkPWl3u0HNGYocNE+IF6qrRaljYKDOVwWEsYYmqxNgVty6t3qmfW7iRd/bQXu7zNTp/KMroJIjXdLjt0kxSfFe/hqZWbygWWAJySwWKR1v/wSPGtod+CSS/j0ww/lAyrFxXJPpfZAIqcj6HUJH+WekwNctBsY/6bnwL4ORjxssrPlX74//QlIjPUfk3PypO/jt0/kOJ26kyZ7XC3SW77+OvDCC/xv2bEDmDvXf7+6OiNGcAH54ovcTzo7m2eQ+/zzUPcsQLIW8tiyq6zAVWZg1k6/ls5A8DbCLNqbi4znnwecSRk7BJEUwhvu57/hBp65ULjteCiPIP8AgWWg64J4c0sjd7V2olBIn+ZtHYA3MROQJccP11zDXUnKy/kL4UcfAR98AMycybMNdgTje42Hgznw3ZHv0CemT+Aj+kTbSHQONpZv5Kmem9PD6WpbuIK727pja2y5fXMa3X5gh94PxI8AekvB6SKD2nNbn8NzW5/DM1ueAQMLPLOaiBXVJgBjXgTG/iuw/cAFn4iTVav5/fz551KiAG/uagooXJlqASBGqq8q4847+XT9em5Ju+kmbtE57zwp7oRoyU038Wva2MitOldeyZ/5AwbIPZXaQ/gNOxIuRPkhMYogiIwEMtJaxuQ0t+ScOiXfT6GQu6/5eyn0idkpcqL6eVz9yCM8VXBhIXD33fwjeOGFdpy3i5CYyOvk3HFHqHvSDjo4s5a3kWfRnuc2UDduHK+Nc/PNwEDfBdQDpqDA+7rKSilVukbDfdpTU/kP7Rl/ZW5Exj1bA385UKqA320AmNMkqevaYsGbxYYsOV0br4MGgVpyfDBmDE+Bv2QJd2t2Tx09N7ByOH4R7mlmu5msOJ1B3AjulmZrAA48yhPEKJTcTa1kFbeOKDXc0vPrtcCof/IMm8deBfSpwJC7fR/fXMmn6kgg45oWq4XFZunGpR7b/SLqjWVd1+rsbyVuidMuv5zHKAH8PeSTT4AIteSuJmiyNsGgMUCrlZ6D3qwyf/kLH5x99ln+3vSms2RaTIzbQBnRgl69eIbgP/+Zv49++SVvVyqBngF4SAYCiZwwRlhy+nnQEULIeBI5BjUXQO4iZ/58XihryhSprbi4HZ0TlpzIvh5X9+jBTbmPPgp8+ilPwahW85TSqantOC/RZfE68uxsFxmdAOBvzmyWERHAjTd2zPlPn5bmY2OByZOlQrfuQmb+fOkeXLQogFoHukT+ssAcvEaEvgdPyxkmeHVXo5ic9hHkxBPBtOQA/Ld5xgz+4rZ/P39hmzOnZa2ctpKdlg0FFGBgFI/TGShVQN9rgWOv8DjiwhVc9NTs5/XMdAk8w+yxV3is1km3Inyj/un/+CIdefxojxlI3YuQB9Iuw2GVrEk9pvrfvhmVldK8cMUEeB2o334D8p2WnCc2PeGyKJY0lCBSE4nISGl7XwHyTzzBn1uffsqfZf36cUEVFaCGO1uZNo0PcH79NY+bio/n/6MBA+A7FjbA31cSOWFKU5NkfvUlcoSLGiDF54h17iPbN90EpKXxF7rnnuNt7qMfrUaMukRlet0kM5O7QLz8MrdK9e5NPwjdGX+WHPcHiHvw5pVX8sQN7cX9fr/rLv4SN2cOfyC5n/u886T5zEyeOMInKj1PJ9+Qx2tRhFkaaeGWNiJ5BC7MuhDbi7Zjc8Fmclfr4gTTkiM499yOKcLriXhDPFZctQJmuxmT+3h2ayY6mBFPAOUbeIFm4f4F8CyiAI8xtlQBpz6R1qmjuBXIH6IIaLRn07s3i01AlhxLjTQvUmC3giq3+qTZ2fJ18+cD/z3GLTnfH/teti7RkIg+fbg1xmr17Q0A8Heou/0YvIiWiAHuYEAxOWGKe/Cnp1ozviw5Yp0Y3ejZE7jgAj5/2WVwjVy4W3J69WrFw85hk36UdM7E8Y0FXuvmxMdzP1YSON0bfzE5QmgkJsprEQwZwqtMtxf3+/kqZ8r/v/+dT90fgqNGyfebMyeAg8ee4zzJKnl7rZ/sHl0A4ZY2JGkInr3wWUzuPVnWTnRNvH2fvA0mdEXmDZ6HK8+5Er1jglSQh5CjjQVm7QJGPsMr2qfO5nXhxrzE16v0wOSPgQt3ANmvApM/A+YVAKmz/B9bWHKcpSKa0y5LjsVtFMrL8X3hbskRrmqCPn2kxAPNidRGQqMBBjlLB23Y0OpTE+3BXxxsAJAlJ0xx/9J6EgdqpRpqpRol9SV4ZccrAIBD5TxHrl6th93Og70A7qKmdt4JkZFSLnh3S85LL/GqtJ5SKLbAVg9XgS4Rr7BpLlATSBVRorsiXr5idDHITstGZVMl9pXta2HJaf4QAlrGnbWFWqfGHjZMemidfz5PEOFuyWk+aJCZGcDB40fyVLwn3uEvBL3nAeWbgK1/AOYXtr/zQUS4pVUZq2RTclfr2giLzYuzXkR2Wjb+uPyPqDJWQR2GGf6ITkSlBYbexz/eSBzHP63BJXI8+5u3z5LjPLYmptXxOIA0iBUdzT/NESmkvbUPH86zc/7wAy8eLYp+Fxfz2jiDB7e6S0QnQZacMMU9TaHB4HkbvVqPkoYS/H3V3/H3VX/H67t4kRaD2iArWtVcuEybxl3hRJCdWg3MmsUFUEC5zB02aV6p974dcVahU+tgUBvQN64v1v15HR46l9doaG7JCUZMVmOjVMTW3f1Mo+HuaeLcCkUb6zaJFNsOM/DLfODLaGDdeYCxqD3d7hSitFHQqXSSyDE5RQ5Zcro04nuTHpuOqelTYXVYO9RVjSBahT9LjpcsagFlVxPH1rft4SAGhXv18rxeZFdr0e608Ix31qktLeXu0+XlwNatfJCsoqJNXSI6CRI5YYp7LRlfIsdbu7vI6d9fvn7yZCnTFMAz7YjRD/egPa84nJ1TKPmoEUE4idPHuYpOimlzS04wisO63+/NR92mTpW7yrUpG07sOUAvKV0qbA3et+2CJEYkkiUnzBCCptbEv0c1ppoOSzpAEK3GjxDpEEuOFyuRP4Qlx5vI8WfJueYa6bnw3Xe8wOqUKcErlkt0HCRywpRALTne2t1f+pqn6ouKkheAmzBBPu8tV7wL5rTkKHV+NiTONuIN8agx1QCQXs7Ey5oQ1sGw5Ljf783dz8aP7yAr0oS3geSZbg0KYNAd7Thg55FoSES1iV+EamM1YnWx5PbUxRGCpsZUA6vdiiZrE1lyiNAhhIhwJ6vaA9RLyQ38xuR4qymlUADWGt6mjWtT14TI8TaA5ismB+Ci5tZb23RqIsTQUyxMEa43gPeRZ5Equjl6td4VnwDwdM7NEfE6AJCVJc2rVMDo0YH2MrgpVInwI04fh9yKXDDGXGKnuSUnGCJHZCIEgKRmScP0+g6yIukSgemrgdNf8hTqyb8D4lqfCSgUJEYk4mD5QTiYA1XGKrLihAGuwQFTjeu7RJYcImTYnQVGlU7vjX0PAKU/ula3y5JjdY5SiYHT6v3A1sALVIvfd28Dwv4sOQCwbBl/jrz1lrR+yhRe/JvoupDICVPUbv85s9lzMJ03S45BI4/J8SRy3C057iIHCCClrsiR75a+GpoYQBPnXE8ubGcr8fp4OJgDDZYGl7tavD4edrskROKD8J7mfr83FzmA9BD0a6X0h1IFZAT+8O0qJEUkgYGh2liNKmMVzul5Tqi7RPjB3ZJDIocIOX48OETszeiU0Tgv4zxsLdyKHUU7AovJcR3b+e5gbwLqAs9caXImmdV5cS7xF5Mj9n3zTWDePF5bZ8gQ4Ior+MAv0XUhkROmuFtvjEbP2wTqrubPktPcj3X4cD+dU4jbinGho9IBMzf52Yk4GxBWmxpTjSwmp75eygqpDYIG9idyhKj39hDs7ogkA6UNpWi0NlLSgTDAZckxS98lclcjQgaz86mXQUxhsZnQawJevOhF3LPmHuwo2hGYJcfRTOS0Eptzd2+/74FYcgSXXMI/RHhAMTlhSntFjnBX0+s9p6B2t+Q0H1n3+yLoXu3Y7qVzxFmJGGmuNdfKYnLcE2kEQ2j4clcDpEQewRBY4YAQNXlVeXyZ3NW6PGTJIboMDrs07yXZkBAzDVb+ctFgaZC1+6yJ4kdA+cOfyPEak+OlnQgfSOSEKXo3/eJN5Bg03mNyxMh2opd3GV8ixy/qKADOAEK7yeemxNmFuyWnxlQDlUKFaG206yEEBNeSExkp/+4I7PbgnTscSIrgyk+InCSDByVIdClidDFQKVRykUOWHCIUMDeR481dzZlgoNHC3UQarY2ydt/H7zqWHCK8IJETpri7mLm74rjjNSbHrU6Opxc+QHJXUyiA2NhWdk6pBrTOh62prJU7E90Z97S3teZaxOpjoVAoXCIDCK7I8WTFAfw/BLs7wnJDlpzwQaFQ8JTsplqXyEkwJIS2UwThJeGQy5Jj8WLJ8XlIZ6YlZVvy+0uDWP5icsakjsGqBatw7+R7Ze1E+EIiJ0xJTpayLebne97Gl7uasP54y8wmLDmxsYCyLXeJ3pmXuuFEG3YmuivCkiPc1cRysEWOcFeL8DIwd7ZbclzuatV5smWiayNSspO7GhFSFG7R9w6Lx00itZFQKpQtRE5AiQcUzpcQh9X3dn5gXhK+CotNlDYKF/W/CAMTB8raifCFEg+EKVotdyOrqgKOH/e8jS+RI0auvb3UCZHT5kxX+mSgLpdEDiHDPY6g1lyLvnF9ASDo7mrCMulN1J/1IocsOWFJvD4eRyuPtqg5Jas3InBv8/a2RxBtQakCd1FnUlbVvguBHlP5fCIvthepiXS5qTVaGqFWqr2+p8gQyYy8CCh/iGy0ZrPn9VqVFmqlGk3WJgBwTSkmJ/whkRPGpKT4Fjm+6uSIlzpvL33ipbDNIkcnLDnH5O2mckCbwF3aiLMOWXY1N0uO+ztXmyyHfhAiytv9Ljhb3/1ETM7p2tOyZaJrE2+IR525DlVGXu2QLDlEyFCoeOyMw6kkMq9psUm0LlpmyQnIVU0cG5COnTAWmFcoX+cDfyIH4Fab5iKHLDnhD7mrhTHJyXy6b5+8vbSUu7D5qpPjb+S6/ZYcp8gp3yhvP/aqPEiROKsQI81FdUWwM7tr2b3WgKVtg3U+8SfqxfmDce5wQLin2Z3fTXJXCw/i9fFgYCioK+DLlHiACBUuIeL9RzRKGyVLPBCwyFE2s+QoNUBEL/4x+K/gHIjIidREtrTkUExO2EMiJ4wR1dkPHpTH5bz2Gn9Z8+Wu5u+lT4gcb4kJ/KJ3dq4uFyj6znnQk0Du8208INEdEJabU7Wn+LKOL7sXtw2FyAnkIdididPHQeU2IkruauGBsNycrDkJQPp+EUSno3ZaPezef0SjtXJLTkCZ1QBA4fzhDpK7GsCtNkYrD1YWU7LkhD8kcsKYzExp/l6eDAQ5OcBzz/F5IXLGpo7FRf0vcj0AAxE5TU2+1/sldqg0v3MRkPM8sP58wNbofR+i2yNeygpq5SPPZMkJLQqFQpaZi9zVwgPx/TlVcwrR2mioyQ2YCBUio6qPZ3yUNgpN1iYwxlrnrqZxbudDQPnC4PTc92nJ0Xqw5FBMTthDIieMGTNGmv/qK2DGDGDyZEmgiJich6c9jFULVmFg4kAoFUpoVVq/7mqiOKK6rc/M+NHSfNNpYO89lISAQIwuBkqFUrLkOIV3sC05DmcGUn+WnLNV5ACS9SZCExFYMDARcsSgQb2lnlzViNAiRI6x2Osm0bpoMHCBY7QaA8usBgAa57GtNW3qmnC7b/QxxhqhiYDRxi04TTaKyeku0LBPGJOdLV/esEG+LF5UTDaTayraRKId8fLXHCGC2ixyovry5APm8jYegOiOKBQKxOpiUW2qBiC9pLnfZ8FwGfNnyRHtZ6u7GiDF4VA8TvjgLmxkSQfO1gwaROjQOi3BPkSOsNyUN5aDgQVuyQlAQPlCFD0vKfG+TaQmEjaHDRa7hWJyuhFkyQljMjOBrCzv632JHOGeY/WSdt7fyHdApM1ux85Ed8U9bkDMu9evCaYlR+UlEU9MTPDOHS4IFzVyVQsf3IUNWXKIoKJQSB9PbS4hUuT1ECIGp6yxTLbsF9exfagUHyQ49VeR9665rDZGq5FicroRJHLCnPnzva8zaLi7mtlmdk1bK3LabMkBgPQrW7apDAGlfCS6L55Ejl4vJbnw5VLQVkRaaruXxH7CnaG6uuPPHS64LDmUdCBs8GrJIYjOxpe1xc5Hj4TlpqyhTLYc8LFtDYC1rtVdC0TkCKtNk7WJYnK6ESRywpzrrms5Oq3TcfNsIJYcbyPX7XZXA4C0i4Ee58rbhi6mGjlnObIXM/d552xpqZcdfY0i+kGIHG+iXpzblztDd+fiARfjluxbcOVQD4MTRJdEZskhkUOEEhE3U5cjb6/eC5j4D6uw3JQ2lMqW/aJ1u7ebi6gARI9wVzMavQ9kRTizwwmRo1VpoVLSgGy4QyInzBk2DLjlFnnbXXcBSUmeRY5IRtAp7moAMPZfgN5Z0CdjAXDOw+08IBHueLLkAMEVGv7udxI5wOVDL8d/5vwHN469MdRdIQLE24ABQXQ6Oqe5pGY/YG2Q2vM/cM26LDmNbbTkAEDNQfm6wm/87p4gJY6UldsAgKNH+bS5JYesON2DoImcJ598EpMnT0ZERATi4uKCdRoCwOOPAxdeyAe0//AH4LHHeLsQOWZn2kWzvaW7WlAtOQAQPwq45Chw6Qlg8keAgnT12Y630WfxMxFKkVNby0f7CCIcIEsO0WXQOs0lDitw/E0+bzoDHH/DtYnIpibc1QLOrqZUA2qnIDqzWWpvONmy4LgHEt08cH/7TZpnDPjGqZFE/I0QORSP0z0I2hunxWLBFVdcgVuamxmIDicuDvjxR/5y9vHHkjARVhthyXGPyRHbBDUmR6CJ4dnWCAL+LTle3dXaQaAiBzi7rTlEeCFSsgNkySFCTJRbFqTc54DSn4EtV8rq5gjLTWljqWw5IHTOhCj570kuaoefApiXQEs33C0537gZftauBU6f5vPCcmO0GWG0GSmzWjchaCLnH//4B+68804MHz484H3MZjPq6upkH/PZnNO1leh08mV3dzWzzQwG5mqLdH5//WWTCiDcgSBahRhxNqgN0Kmlm1YIDU/BoVVV7Tunv5gcX+4MBw6079wEESwUCoVroIAsOURIiR4ozRuLefHv8g3yTbTNLDmBxuQAkoiy1gJ77gUOLZMsRn7o3VuaX7UKOHgQsNmApUuldrLkdE+6lO/QsmXLEBsbK/ssW7Ys1N0KW1zuajazy2VNZFyLjeXbiMKhzfH3UkgQbUW8lLlbcQBJ5Jw6BdS5xZJWV8tdDNqCP8uluyVnzx75uu+/b9+5CSKYbL5uM/b+dS/mDJwT6q4QZzMRaZJLmRfaHJMDANEDpPnjbwD7Hwx415495b/xV1wBnHcesG2b1EYxOd2TLpXm6oEHHsBdd90la9M1N08QAeNuyREua6JN1AWprOR+qc0tNkLk2Gyd0lXiLGJC7wm4c+Kd6BnZU9YuHkKMcVFz/vl8+fvv5Q+otiDq8AQicn78EbjnHj6/Zw9w5Ej7zk0QwWRIjyGh7gJxNhBIgdn40cCZX7yubnNMDgBEDfC/jQ8GD5ZETW5uy/XCctNoaYTJZiJLTjehVZacxYsXQ6FQ+Pzkerp7AkSn0yEmJkb2IZHTdoTVxmT3LnJsNs8pFUUMA4kcoqPJTsvGC7NewOKpi2Xt7i5j77wjzb/xBtqNuN+9JRVITpbm162THoLPP0/F4wmCIAIiaYrP1cJy02htlC0HRPyotvYKADB0qO/1wnJTaazkyxST0y1olci5++67kZOT4/OTlZXl/0BEpyBzV3MWBNWreJtwVwOAM2da7kuWHKKz6euWm+J//wMKCoAPPgB+EQODjHn/+CHaOWBYWel5fUwMd2kQp/n734H77uOJPAiCIIgA6DnNywruKtJc1LRK5CROaFch8cmTfa8XlpvKpkrZMhHetMpdrUePHujRo0ew+kJ0MJ7c1YR1R4xsA0BFBTBokHxff9moCKKjGeDmjWCxAP37d9z9J+73+np+bK3W8/nLy/n8Tz/xD0EQBBEgKRcAEb2BpkKpLWYwENEHQMtEA61KPKCJAuJGAtW7m60ILDvSNG/6y4mw3FQ0VfBlisnpFgQt8UBBQQH27t2LgoIC2O127N27F3v37kVDQ4P/nYkOIZCYHIAsOUTXICtLuu+AjhXY7ve7N2vOwIGe2wmCIIgAUKqBAbfK28551BX02y5LDgD0XdiyLXl6QLv278+fMc0RA17CclNhrJAtE+FN0ETOo48+itGjR2PJkiVoaGjA6NGjMXr0aOzcuTNYpySaoVaqoVaqYbZL2dWEyHF3V2tel6SsjEQO0fnodHKXtY4k2m3AsKLC8zYjRgTn3ARBEGcN/f8KxI3i8/3+AmRe41qlUWmgU0lx1q0WOVnXyjO4RfYF0q8OePfbbmvZdtllzkNpyJLTHQmayHnvvffAGGvxmT59erBOSXhAr9b7teQ0zx61aROg0fB5clcjOpPx44Nz3ObumZ6YNCk45yYIgjhr0MYBF2wFLjkGjG9Zx0YIG4PaAJWylTE2mhjgnIf4vDoSmPg+oPLge+yFv/xFnklzxgwpVodicronXapODtHxeBM5UVGStebQIfk+GzZIKXfJkkN0JsESGr5EjrjHx4wB9PrgnJ8gCOKsQW0Aovt7XCXSRrcqfbQ7QxcD528ELj4M9Dy3VbtGRgKvv869Bvr0Ad56y22dlrKrdUdI5HRzmoscg5onHlAouNABgM2bpeKLxcU8l3yk8/ttNnd2j4mzmXO9PLOU7fylcndXKyiQrxPFPzUaYNaslvv27NmyjSAIgmg9wpLTalc1d3pOAyLT27TrlVcCRUXA8ePyGB2y5HRPSOR0cwxqgzyFtFoaqha1QYxGYPlyPv/ee4DdLgmgmhovB1YopE/zNoJoI6NG8Y87Oh0wcWL7juvLPXPzZml+0aKW+/75z+07N0EQBMERGdValVmtg0lMlFzyBQa1AQooYHVwH32KyekekMjp5nhzVwOAjAxpu5dfBj79FHjqKb4sRI6nQqEEEUyaC40rr5T7UbeFqChJf+/bJ7Uzxt0zBTNn8srYgrlzgWHD2ndugiAIgtMhlpwgoFAoXCU2ALLkdBdI5HRzAhU5e/cCf/gD0MgLEbvc1UjkEJ3NH/4A9O7N56Ojgccea/8xFQrJZe2334DTp/n81q3cRdN9u3ffBRISeN2cV19t/7kJgiAITrtjcoKIu/WGYnK6ByRyujl6tV6WQtp9pMJd5DTH3V0tgILyBNFhREQAW7bwdJ8//QRkZnbMcVNT+ZQx4Msv+fwbb7TcbuJELnyOHJHEFkEQBNF+uqolB5Bbb8iS0z1Qh7oDRHAxaAyw2C0wWo0A5JYcXzVJhCXHbudV4t1jGggi2KSncxfKjqRPHyke54UXgPx84IMPgOzsltvqdC3bCIIgiPYxLX0aTDYTzss4L9RdaYG79YZicroHJHK6OULU1JhqZMsAMHy49/2i3AZZqqtJ5BDhT7pbMp6iIuCVV0LXF4IgiLORG8bcgBvG3BDqbniELDndD3JX6+YIUVNrrpUtA8DQoYDWSx2t5iLHHbu9Q7tIEJ1CetsyjhIEQRBdGV/ZXluR8ZVicrofJHK6Oc1FjqiTA/AUiuec43m/SLfvd0mJfF1ubod2kSA6hY6K7SEIgiC6H2TJ6X6QyOnmCFHjyV0NAKZM8bCPQW7Jyc+Xr9+1qyN7SBCdw9Choe4BQRAE0VWhmJzuB4mcbo7LkmNq6a4GAFdf3XKfCy+Ui5wTJ+Trd+/u0C4SRKcwbBigpF88giAIwgPCeqNSqKBTU/aZ7gA98rs5vmJyAGDy5JZuPFddJXdXc7fcHDgAlJcHo6cEEVwMBqB//1D3giAIguiKCOsNuap1H0jkdHOaZ1dzr5MD8Ji8P/5RWr74Yh6n455Nbft2wMzL7GDVqmD2liDaQQCBp+PGtdxNTTkmCYIgznqEuCGR030gkdPNETE5taZaKBVKaFUt06k99BBw/vl8lPu113hbfLyUec1k4sUZ7Xbgo4+cOzHm/UMQXZTLLmvZNnVq5/eDIAiC6FoISw5lVus+0BhmN0dYcow2oyyzmmwbPa8s35zUVODUKT5/553A6NHcXW3YsGD1liCCy+zZPN6soUFqmz8/ZN0hCIIgughkyel+kCWnm+Meg9M8HscfqanS/P79wPvvd1SvCCI0GAzAJZdIyyNGeM4wSBAEQZxdZMVnYUKvCRifNj7UXSE6CLLkdHPchU3zeBx/pKV1dG8IIvQ8+CDw/ffcmvPMM6HuDUEQBNEVuOKcK3DFOVeEuhtEB0Iip5vjLmzaY8khiO7C8OHAL78A9fUUj0MQBEEQ3RUSOd2c9rirZWR0dG8IomswcmSoe0AQBEEQRDChmJxuTntEzqBBHd0bgiAIgiAIggg+JHK6ObKYHC/Z1bwxeHBH94YgCIIgCIIggg+5q3Vz3IVNay05WVmARgNYrR3dK4IgCIIgiA6A6vMRXiBLTjenPe5qajUwYEDLdvei8gRBEARBEATR1SCR081pj8gBgN/9rmWbJ+FDEARBEARBEF0FEjndnPbUyQGAiy9u2TZzZnt6RBAEQRAEQRDBhURON0dWJ0fVekvOjBm8SrwgMRGYMKEjekYQBEEQBEEQwYFETjenve5qer3cZW3BAp6MgCAIgiAIgiC6KkETOSdPnsQNN9yAvn37wmAwoF+/fliyZAksFkuwTkl4oL0iBwCWLAG0WiA6Grj99o7qGUF0MIx5/xAEQRAEcVYRtBTSubm5cDgc+O9//4v+/fvj4MGDuPHGG9HY2IjnnnsuWKclmqFWqqFSqGBn9jbF5ADAuHHAmjVAbCxPK00QBEEQBEEQXZmgiZyLLroIF110kWs5KysLR44cwWuvvUYip5MxaAxosDS02ZIDAOed14EdIgiCIAiCIIgg0qnFQGtra5GQkOB1vdlshtlslrXpdDrodLpgd61bo1fr2y1yCIIgCIIgCCJc6LTEA3l5efj3v/+Nv/71r163WbZsGWJjY2WfZcuWdVYXuy1C3JDIIQiCIAiCIM4GWi1yFi9eDIVC4fOTm5sr26eoqAgXXXQRrrjiCtx4441ej/3AAw+gtrZW9nnggQda/1cRMgxqHotDIocgCIIgCII4G2i1yLn77ruRk5Pj85PlFp1eXFyMGTNmYPLkyXjjjTd8Hlun0yEmJkb2cXdVM5vNWLp0aQuXNsI3B246gCVsCRYMXRDqroQddM+1DbpubYOuW9uha9c26Lq1DbpubYeuXdug69Z6FIwFL79qUVERZsyYgbFjx+Kjjz6CSqVq1/Hq6uoQGxuL2tpaxMTEdFAvuz903doOXbu2QdetbdB1azt07doGXbe2Qdet7dC1axt03VpP0BIPFBUVYfr06cjIyMBzzz2HM2fOuNalpKQE67QEQRAEQRAEQZzlBE3krF27Fnl5ecjLy0Pv3r1l64JoPCIIgiAIgiAI4iwnaNnVrr32WjDGPH4IgiAIgiAIgiCCRaelkO4IdDodlixZQnVzWgldt7ZD165t0HVrG3Td2g5du7ZB161t0HVrO3Tt2gZdt9YT1MQDBEEQBEEQBEEQnU1YWXIIgiAIgiAIgiD8QSKHIAiCIAiCIIhuBYkcgiAIgiAIgiC6FSRyCIIgCIIgCILoVpDIIQiCIAiCIAiiWxE2IufVV19FZmYm9Ho9JkyYgB07doS6S12OTZs24dJLL0VaWhoUCgW+/vpr2XrGGB599FGkpqbCYDBg5syZOHbsWGg624VYtmwZxo0bh+joaPTs2RPz58/HkSNHZNuYTCYsWrQIiYmJiIqKwuWXX46ysrIQ9bhr8Nprr2HEiBGIiYlBTEwMJk2ahFWrVrnW0zULjKeffhoKhQJ33HGHq42unWeWLl0KhUIh+wwePNi1nq6bd4qKivDHP/4RiYmJMBgMGD58OHbu3OlaT88Hz2RmZra45xQKBRYtWgSA7jlv2O12PPLII+jbty8MBgP69euHxx9/XFYrke45z9TX1+OOO+5ARkYGDAYDJk+ejN9++821nq5bK2BhwGeffca0Wi1755132KFDh9iNN97I4uLiWFlZWai71qX44Ycf2EMPPcSWL1/OALAVK1bI1j/99NMsNjaWff3112zfvn1s7ty5rG/fvsxoNIamw12EWbNmsXfffZcdPHiQ7d27l1188cUsPT2dNTQ0uLa5+eabWZ8+fdi6devYzp072cSJE9nkyZND2OvQ8+2337Lvv/+eHT16lB05coQ9+OCDTKPRsIMHDzLG6JoFwo4dO1hmZiYbMWIEu/32213tdO08s2TJEnbOOeewkpIS1+fMmTOu9XTdPFNVVcUyMjLYtddey3799Vd24sQJ9uOPP7K8vDzXNvR88Ex5ebnsflu7di0DwNavX88Yo3vOG08++SRLTExkK1euZPn5+ezLL79kUVFR7OWXX3ZtQ/ecZ6688ko2dOhQtnHjRnbs2DG2ZMkSFhMTwwoLCxljdN1aQ1iInPHjx7NFixa5lu12O0tLS2PLli0LYa+6Ns1FjsPhYCkpKezZZ591tdXU1DCdTsc+/fTTEPSw61JeXs4AsI0bNzLG+HXSaDTsyy+/dG2Tk5PDALBt27aFqptdkvj4ePbWW2/RNQuA+vp6NmDAALZ27Vp23nnnuUQOXTvvLFmyhI0cOdLjOrpu3rn//vvZ1KlTva6n50Pg3H777axfv37M4XDQPeeDOXPmsOuvv17Wdtlll7EFCxYwxuie80ZTUxNTqVRs5cqVsvYxY8awhx56iK5bK+ny7moWiwW7du3CzJkzXW1KpRIzZ87Etm3bQtiz8CI/Px+lpaWy6xgbG4sJEybQdWxGbW0tACAhIQEAsGvXLlitVtm1Gzx4MNLT0+naObHb7fjss8/Q2NiISZMm0TULgEWLFmHOnDmyawTQ/eaPY8eOIS0tDVlZWViwYAEKCgoA0HXzxbfffovs7GxcccUV6NmzJ0aPHo0333zTtZ6eD4FhsVjw0Ucf4frrr4dCoaB7zgeTJ0/GunXrcPToUQDAvn37sHnzZsyePRsA3XPesNlssNvt0Ov1snaDwYDNmzfTdWsl6lB3wB8VFRWw2+1ITk6WtScnJyM3NzdEvQo/SktLAcDjdRTrCMDhcOCOO+7AlClTMGzYMAD82mm1WsTFxcm2pWsHHDhwAJMmTYLJZEJUVBRWrFiBoUOHYu/evXTNfPDZZ59h9+7dMj9rAd1v3pkwYQLee+89DBo0CCUlJfjHP/6Bc889FwcPHqTr5oMTJ07gtddew1133YUHH3wQv/32G2677TZotVosXLiQng8B8vXXX6OmpgbXXnstAPqu+mLx4sWoq6vD4MGDoVKpYLfb8eSTT2LBggUA6J3EG9HR0Zg0aRIef/xxDBkyBMnJyfj000+xbds29O/fn65bK+nyIocgOpNFixbh4MGD2Lx5c6i7EhYMGjQIe/fuRW1tLf73v/9h4cKF2LhxY6i71aU5ffo0br/9dqxdu7bFaB3hGzEKDAAjRozAhAkTkJGRgS+++AIGgyGEPevaOBwOZGdn46mnngIAjB49GgcPHsTrr7+OhQsXhrh34cPbb7+N2bNnIy0tLdRd6fJ88cUX+Pjjj/HJJ5/gnHPOwd69e3HHHXcgLS2N7jk/fPjhh7j++uvRq1cvqFQqjBkzBtdccw127doV6q6FHV3eXS0pKQkqlapFtpKysjKkpKSEqFfhh7hWdB29c+utt2LlypVYv349evfu7WpPSUmBxWJBTU2NbHu6doBWq0X//v0xduxYLFu2DCNHjsTLL79M18wHu3btQnl5OcaMGQO1Wg21Wo2NGzfiX//6F9RqNZKTk+naBUhcXBwGDhyIvLw8uud8kJqaiqFDh8rahgwZ4nL1o+eDf06dOoWffvoJf/nLX1xtdM95595778XixYtx9dVXY/jw4fjTn/6EO++8E8uWLQNA95wv+vXrh40bN6KhoQGnT5/Gjh07YLVakZWVRdetlXR5kaPVajF27FisW7fO1eZwOLBu3TpMmjQphD0LL/r27YuUlBTZdayrq8Ovv/561l9HxhhuvfVWrFixAj///DP69u0rWz927FhoNBrZtTty5AgKCgrO+mvXHIfDAbPZTNfMB+effz4OHDiAvXv3uj7Z2dlYsGCBa56uXWA0NDTg+PHjSE1NpXvOB1OmTGmRFv/o0aPIyMgAQM+HQHj33XfRs2dPzJkzx9VG95x3mpqaoFTKXzFVKhUcDgcAuucCITIyEqmpqaiursaPP/6IefPm0XVrLaHOfBAIn332GdPpdOy9995jhw8fZjfddBOLi4tjpaWloe5al6K+vp7t2bOH7dmzhwFgL7zwAtuzZw87deoUY4ynHYyLi2PffPMN279/P5s3bx6lHWSM3XLLLSw2NpZt2LBBliq0qanJtc3NN9/M0tPT2c8//8x27tzJJk2axCZNmhTCXoeexYsXs40bN7L8/Hy2f/9+tnjxYqZQKNiaNWsYY3TNWoN7djXG6Np54+6772YbNmxg+fn5bMuWLWzmzJksKSmJlZeXM8bounljx44dTK1WsyeffJIdO3aMffzxxywiIoJ99NFHrm3o+eAdu93O0tPT2f33399iHd1znlm4cCHr1auXK4X08uXLWVJSErvvvvtc29A955nVq1ezVatWsRMnTrA1a9awkSNHsgkTJjCLxcIYo+vWGsJC5DDG2L///W+Wnp7OtFotGz9+PNu+fXuou9TlWL9+PQPQ4rNw4ULGGE/Z+Mgjj7Dk5GSm0+nY+eefz44cORLaTncBPF0zAOzdd991bWM0Gtnf/vY3Fh8fzyIiItjvf/97VlJSErpOdwGuv/56lpGRwbRaLevRowc7//zzXQKHMbpmraG5yKFr55mrrrqKpaamMq1Wy3r16sWuuuoqWa0Xum7e+e6779iwYcOYTqdjgwcPZm+88YZsPT0fvPPjjz8yAB6vB91znqmrq2O33347S09PZ3q9nmVlZbGHHnqImc1m1zZ0z3nm888/Z1lZWUyr1bKUlBS2aNEiVlNT41pP1y1wFIy5lZ8lCIIgCIIgCIIIc7p8TA5BEARBEARBEERrIJFDEARBEARBEES3gkQOQRAEQRAEQRDdChI5BEEQBEEQBEF0K0jkEARBEARBEATRrSCRQxAEQRAEQRBEt4JEDkEQBEEQBEEQ3QoSOQRBEARBEARBdCtI5BAEQRAEQRAE0a0gkUMQBEEQBEEQRLeCRA5BEARBEARBEN2K/wfsfbmyURBaBQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x200 with 1 Axes>"
       ]
@@ -5355,15 +5712,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:06.732828Z",
-     "iopub.status.busy": "2025-11-21T06:33:06.732690Z",
-     "iopub.status.idle": "2025-11-21T06:33:10.008101Z",
-     "shell.execute_reply": "2025-11-21T06:33:10.007545Z"
-    }
-   },
+   "execution_count": 51,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5401,7 +5751,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>3874</th>\n",
+       "      <th>3951</th>\n",
        "      <td>ZNF746.H13CORE.0.PSG.A</td>\n",
        "      <td>neg.SPI1@1917</td>\n",
        "      <td>165744</td>\n",
@@ -5410,12 +5760,12 @@
        "      <td>26.355452</td>\n",
        "      <td>7.435330e-10</td>\n",
        "      <td>AGGGAGGAGGGAGGAAGGTGGGAGGA</td>\n",
-       "      <td>-0.010775</td>\n",
-       "      <td>-0.016253</td>\n",
+       "      <td>-0.010761</td>\n",
+       "      <td>-0.016230</td>\n",
        "      <td>1904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3453</th>\n",
+       "      <th>3530</th>\n",
        "      <td>ZN263.H13CORE.1.P.B</td>\n",
        "      <td>neg.SPI1@1898</td>\n",
        "      <td>165732</td>\n",
@@ -5424,12 +5774,12 @@
        "      <td>24.008722</td>\n",
        "      <td>1.311946e-09</td>\n",
        "      <td>GGGGAGGAGGACAGGGAGGAG</td>\n",
-       "      <td>-0.006567</td>\n",
-       "      <td>-0.016637</td>\n",
+       "      <td>-0.006555</td>\n",
+       "      <td>-0.016612</td>\n",
        "      <td>1892</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>781</th>\n",
+       "      <th>854</th>\n",
        "      <td>ZN479.H13CORE.0.P.C</td>\n",
        "      <td>neg.SPI1@-174</td>\n",
        "      <td>163668</td>\n",
@@ -5438,12 +5788,12 @@
        "      <td>22.937369</td>\n",
        "      <td>2.837623e-09</td>\n",
        "      <td>GCCCCCAAAGTCATCCCT</td>\n",
-       "      <td>-0.007155</td>\n",
-       "      <td>-0.013835</td>\n",
+       "      <td>-0.007137</td>\n",
+       "      <td>-0.013795</td>\n",
        "      <td>-172</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1036</th>\n",
+       "      <th>1109</th>\n",
        "      <td>ZNF746.H13CORE.0.PSG.A</td>\n",
        "      <td>neg.SPI1@-191</td>\n",
        "      <td>163639</td>\n",
@@ -5452,12 +5802,12 @@
        "      <td>24.462995</td>\n",
        "      <td>3.833248e-09</td>\n",
        "      <td>TCTCCCTCCCATCCTCCCTCCCCAGC</td>\n",
-       "      <td>-0.002449</td>\n",
-       "      <td>-0.001297</td>\n",
+       "      <td>-0.002451</td>\n",
+       "      <td>-0.001303</td>\n",
        "      <td>-201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3545</th>\n",
+       "      <th>3622</th>\n",
        "      <td>ZNF746.H13CORE.0.PSG.A</td>\n",
        "      <td>neg.SPI1@1898</td>\n",
        "      <td>165732</td>\n",
@@ -5466,8 +5816,8 @@
        "      <td>23.523391</td>\n",
        "      <td>7.853286e-09</td>\n",
        "      <td>GGGGAGGAGGACAGGGAGGAGGGAGG</td>\n",
-       "      <td>-0.005327</td>\n",
-       "      <td>-0.010747</td>\n",
+       "      <td>-0.005316</td>\n",
+       "      <td>-0.010729</td>\n",
        "      <td>1892</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -5485,21 +5835,21 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1088</th>\n",
+       "      <th>1446</th>\n",
        "      <td>CREB3.H13CORE.0.SM.B</td>\n",
-       "      <td>neg.SPI1@-21</td>\n",
+       "      <td>neg.SPI1@-22</td>\n",
        "      <td>163819</td>\n",
        "      <td>163833</td>\n",
        "      <td>+</td>\n",
        "      <td>1.754682</td>\n",
        "      <td>4.999340e-04</td>\n",
        "      <td>GCGGTGATGTCACC</td>\n",
-       "      <td>-0.206348</td>\n",
-       "      <td>-0.585193</td>\n",
+       "      <td>-0.206573</td>\n",
+       "      <td>-0.585771</td>\n",
        "      <td>-21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2067</th>\n",
+       "      <th>2140</th>\n",
        "      <td>RXRB.H13CORE.2.PS.A</td>\n",
        "      <td>neg.SPI1@1182</td>\n",
        "      <td>165019</td>\n",
@@ -5508,12 +5858,12 @@
        "      <td>12.213856</td>\n",
        "      <td>NaN</td>\n",
        "      <td>CCATGACCTCT</td>\n",
-       "      <td>-0.008323</td>\n",
-       "      <td>-0.024233</td>\n",
+       "      <td>-0.008334</td>\n",
+       "      <td>-0.024255</td>\n",
        "      <td>1179</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2913</th>\n",
+       "      <th>2990</th>\n",
        "      <td>KLF7.H13CORE.0.P.B</td>\n",
        "      <td>neg.SPI1@1813</td>\n",
        "      <td>165662</td>\n",
@@ -5522,12 +5872,12 @@
        "      <td>15.217368</td>\n",
        "      <td>NaN</td>\n",
        "      <td>GGGGGCGGGG</td>\n",
-       "      <td>0.008973</td>\n",
-       "      <td>0.025625</td>\n",
+       "      <td>0.008977</td>\n",
+       "      <td>0.025628</td>\n",
        "      <td>1822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2986</th>\n",
+       "      <th>3063</th>\n",
        "      <td>KLF7.H13CORE.0.P.B</td>\n",
        "      <td>neg.SPI1@1832</td>\n",
        "      <td>165662</td>\n",
@@ -5536,12 +5886,12 @@
        "      <td>15.217368</td>\n",
        "      <td>NaN</td>\n",
        "      <td>GGGGGCGGGG</td>\n",
-       "      <td>0.008973</td>\n",
-       "      <td>0.025625</td>\n",
+       "      <td>0.008977</td>\n",
+       "      <td>0.025628</td>\n",
        "      <td>1822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7451</th>\n",
+       "      <th>7528</th>\n",
        "      <td>KLF7.H13CORE.0.P.B</td>\n",
        "      <td>pos.SPI1@1820</td>\n",
        "      <td>165662</td>\n",
@@ -5550,59 +5900,59 @@
        "      <td>15.217368</td>\n",
        "      <td>NaN</td>\n",
        "      <td>GGGGGCGGGG</td>\n",
-       "      <td>0.008973</td>\n",
-       "      <td>0.025625</td>\n",
+       "      <td>0.008977</td>\n",
+       "      <td>0.025628</td>\n",
        "      <td>1822</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8556 rows × 11 columns</p>\n",
+       "<p>8391 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                       motif           peak   start     end strand      score  \\\n",
-       "3874  ZNF746.H13CORE.0.PSG.A  neg.SPI1@1917  165744  165770      -  26.355452   \n",
-       "3453     ZN263.H13CORE.1.P.B  neg.SPI1@1898  165732  165753      +  24.008722   \n",
-       "781      ZN479.H13CORE.0.P.C  neg.SPI1@-174  163668  163686      -  22.937369   \n",
-       "1036  ZNF746.H13CORE.0.PSG.A  neg.SPI1@-191  163639  163665      +  24.462995   \n",
-       "3545  ZNF746.H13CORE.0.PSG.A  neg.SPI1@1898  165732  165758      -  23.523391   \n",
+       "3951  ZNF746.H13CORE.0.PSG.A  neg.SPI1@1917  165744  165770      -  26.355452   \n",
+       "3530     ZN263.H13CORE.1.P.B  neg.SPI1@1898  165732  165753      +  24.008722   \n",
+       "854      ZN479.H13CORE.0.P.C  neg.SPI1@-174  163668  163686      -  22.937369   \n",
+       "1109  ZNF746.H13CORE.0.PSG.A  neg.SPI1@-191  163639  163665      +  24.462995   \n",
+       "3622  ZNF746.H13CORE.0.PSG.A  neg.SPI1@1898  165732  165758      -  23.523391   \n",
        "...                      ...            ...     ...     ...    ...        ...   \n",
-       "1088    CREB3.H13CORE.0.SM.B   neg.SPI1@-21  163819  163833      +   1.754682   \n",
-       "2067     RXRB.H13CORE.2.PS.A  neg.SPI1@1182  165019  165030      -  12.213856   \n",
-       "2913      KLF7.H13CORE.0.P.B  neg.SPI1@1813  165662  165672      +  15.217368   \n",
-       "2986      KLF7.H13CORE.0.P.B  neg.SPI1@1832  165662  165672      +  15.217368   \n",
-       "7451      KLF7.H13CORE.0.P.B  pos.SPI1@1820  165662  165672      +  15.217368   \n",
+       "1446    CREB3.H13CORE.0.SM.B   neg.SPI1@-22  163819  163833      +   1.754682   \n",
+       "2140     RXRB.H13CORE.2.PS.A  neg.SPI1@1182  165019  165030      -  12.213856   \n",
+       "2990      KLF7.H13CORE.0.P.B  neg.SPI1@1813  165662  165672      +  15.217368   \n",
+       "3063      KLF7.H13CORE.0.P.B  neg.SPI1@1832  165662  165672      +  15.217368   \n",
+       "7528      KLF7.H13CORE.0.P.B  pos.SPI1@1820  165662  165672      +  15.217368   \n",
        "\n",
        "           p-value                 matched_seq  site_attr_score  \\\n",
-       "3874  7.435330e-10  AGGGAGGAGGGAGGAAGGTGGGAGGA        -0.010775   \n",
-       "3453  1.311946e-09       GGGGAGGAGGACAGGGAGGAG        -0.006567   \n",
-       "781   2.837623e-09          GCCCCCAAAGTCATCCCT        -0.007155   \n",
-       "1036  3.833248e-09  TCTCCCTCCCATCCTCCCTCCCCAGC        -0.002449   \n",
-       "3545  7.853286e-09  GGGGAGGAGGACAGGGAGGAGGGAGG        -0.005327   \n",
+       "3951  7.435330e-10  AGGGAGGAGGGAGGAAGGTGGGAGGA        -0.010761   \n",
+       "3530  1.311946e-09       GGGGAGGAGGACAGGGAGGAG        -0.006555   \n",
+       "854   2.837623e-09          GCCCCCAAAGTCATCCCT        -0.007137   \n",
+       "1109  3.833248e-09  TCTCCCTCCCATCCTCCCTCCCCAGC        -0.002451   \n",
+       "3622  7.853286e-09  GGGGAGGAGGACAGGGAGGAGGGAGG        -0.005316   \n",
        "...            ...                         ...              ...   \n",
-       "1088  4.999340e-04              GCGGTGATGTCACC        -0.206348   \n",
-       "2067           NaN                 CCATGACCTCT        -0.008323   \n",
-       "2913           NaN                  GGGGGCGGGG         0.008973   \n",
-       "2986           NaN                  GGGGGCGGGG         0.008973   \n",
-       "7451           NaN                  GGGGGCGGGG         0.008973   \n",
+       "1446  4.999340e-04              GCGGTGATGTCACC        -0.206573   \n",
+       "2140           NaN                 CCATGACCTCT        -0.008334   \n",
+       "2990           NaN                  GGGGGCGGGG         0.008977   \n",
+       "3063           NaN                  GGGGGCGGGG         0.008977   \n",
+       "7528           NaN                  GGGGGCGGGG         0.008977   \n",
        "\n",
        "      motif_attr_score from_tss  \n",
-       "3874         -0.016253     1904  \n",
-       "3453         -0.016637     1892  \n",
-       "781          -0.013835     -172  \n",
-       "1036         -0.001297     -201  \n",
-       "3545         -0.010747     1892  \n",
+       "3951         -0.016230     1904  \n",
+       "3530         -0.016612     1892  \n",
+       "854          -0.013795     -172  \n",
+       "1109         -0.001303     -201  \n",
+       "3622         -0.010729     1892  \n",
        "...                ...      ...  \n",
-       "1088         -0.585193      -21  \n",
-       "2067         -0.024233     1179  \n",
-       "2913          0.025625     1822  \n",
-       "2986          0.025625     1822  \n",
-       "7451          0.025625     1822  \n",
+       "1446         -0.585771      -21  \n",
+       "2140         -0.024255     1179  \n",
+       "2990          0.025628     1822  \n",
+       "3063          0.025628     1822  \n",
+       "7528          0.025628     1822  \n",
        "\n",
-       "[8556 rows x 11 columns]"
+       "[8391 rows x 11 columns]"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5621,15 +5971,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:10.009453Z",
-     "iopub.status.busy": "2025-11-21T06:33:10.009308Z",
-     "iopub.status.idle": "2025-11-21T06:33:10.050722Z",
-     "shell.execute_reply": "2025-11-21T06:33:10.050157Z"
-    }
-   },
+   "execution_count": 52,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5637,7 +5980,7 @@
        "torch.Size([1, 5, 524288])"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5650,30 +5993,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:33:10.052042Z",
-     "iopub.status.busy": "2025-11-21T06:33:10.051892Z",
-     "iopub.status.idle": "2025-11-21T06:33:11.111093Z",
-     "shell.execute_reply": "2025-11-21T06:33:11.110606Z"
-    }
-   },
+   "execution_count": 53,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "tensor([[[-0.0000e+00,  0.0000e+00, -0.0000e+00,  ..., -0.0000e+00,\n",
        "           0.0000e+00,  0.0000e+00],\n",
-       "         [-0.0000e+00, -0.0000e+00, -2.6888e-05,  ...,  0.0000e+00,\n",
+       "         [-0.0000e+00, -0.0000e+00, -2.7133e-05,  ...,  0.0000e+00,\n",
        "           0.0000e+00,  0.0000e+00],\n",
-       "         [-0.0000e+00,  1.2651e-04,  0.0000e+00,  ...,  3.7016e-05,\n",
-       "          -0.0000e+00,  1.5136e-05],\n",
-       "         [ 1.7333e-04, -0.0000e+00,  0.0000e+00,  ..., -0.0000e+00,\n",
-       "           1.2473e-05, -0.0000e+00]]], device='cuda:0')"
+       "         [-0.0000e+00,  1.2640e-04,  0.0000e+00,  ...,  3.7306e-05,\n",
+       "          -0.0000e+00,  1.5127e-05],\n",
+       "         [ 1.7314e-04, -0.0000e+00,  0.0000e+00,  ..., -0.0000e+00,\n",
+       "           1.2241e-05, -0.0000e+00]]], device='cuda:0')"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5692,13 +6028,20 @@
     "\n",
     "attrs  # (batch_size, 4, seq_len) gene mask is removed from final attributions"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "decima",
+   "display_name": "Python (decima)",
    "language": "python",
-   "name": "python3"
+   "name": "decima"
   },
   "language_info": {
    "codemirror_mode": {
@@ -5710,9 +6053,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/docs/tutorials/2-variant-effect-prediction.ipynb b/docs/tutorials/2-variant-effect-prediction.ipynb
index fb61bce..82ee72d 100644
--- a/docs/tutorials/2-variant-effect-prediction.ipynb
+++ b/docs/tutorials/2-variant-effect-prediction.ipynb
@@ -74,30 +74,63 @@
       "      genes and variants mapped based on these column not based on the genomic\n",
       "      locus based on the annotaiton.\n",
       "\n",
+      "      >>> decima vep -v \"data/sample.vcf\" -o \"vep_results.parquet\" --distance-\n",
+      "      type tss --min-distance 50000 --max-distance 100000 # predict for\n",
+      "      variants within 50kb of the TSS and 100kb of the TSS\n",
+      "\n",
+      "      >>> decima vep -v \"data/sample.vcf\" -o \"vep_results.parquet\" --save-\n",
+      "      replicates # save the replicates in the output parquet file\n",
+      "\n",
+      "      >>> decima vep -v \"data/sample.vcf\" -o \"vep_results.parquet\" --genome\n",
+      "      \"hg38\" # use hg38 genome build\n",
+      "\n",
+      "      >>> decima vep -v \"data/sample.vcf\" -o \"vep_results.parquet\" --genome\n",
+      "      \"path/to/fasta/hg38.fa\"  # use custom genome build\n",
+      "\n",
       "Options:\n",
-      "  -v, --variants PATH       Path to the variant file .vcf file\n",
-      "  -o, --output_pq PATH      Path to the output parquet file.\n",
-      "  --tasks TEXT              Tasks to predict. If not provided, all tasks will\n",
-      "                            be predicted.\n",
-      "  --chunksize INTEGER       Number of variants to process in each chunk.\n",
-      "                            Loading variants in chunks is more memory\n",
-      "                            efficient.This chuck of variants will be process\n",
-      "                            and saved to output parquet file before contineus\n",
-      "                            to next chunk. Default: 10_000.\n",
-      "  --model INTEGER           Model to use for variant effect prediction either\n",
-      "                            replicate number or path to the model.\n",
-      "  --device TEXT             Device to use. Default: None which automatically\n",
-      "                            selects the best device.\n",
-      "  --batch-size INTEGER      Batch size for the model. Default: 1.\n",
-      "  --num-workers INTEGER     Number of workers for the loader. Default: 1.\n",
-      "  --max-distance FLOAT      Maximum distance from the TSS. Default: 524288.\n",
-      "  --max-distance-type TEXT  Type of maximum distance. Default: tss.\n",
-      "  --include-cols TEXT       Columns to include in the output in the original\n",
-      "                            tsv file to include in the output parquet file.\n",
-      "                            Default: None.\n",
-      "  --gene-col TEXT           Column name for gene names. Default: None.\n",
-      "  --genome TEXT             Genome build. Default: hg38.\n",
-      "  --help                    Show this message and exit.\n",
+      "  -v, --variants PATH        Path to the variant .vcf file. VCF file needs to\n",
+      "                             be normalized. Try normalizing th vcf file in\n",
+      "                             case of an error. `bcftools norm -f ref.fasta\n",
+      "                             input.vcf.gz -o output.vcf.gz`\n",
+      "  -o, --output_pq PATH       Path to the output parquet file.\n",
+      "  --tasks TEXT               Tasks to predict. If not provided, all tasks will\n",
+      "                             be predicted.\n",
+      "  --chunksize INTEGER        Number of variants to process in each chunk.\n",
+      "                             Loading variants in chunks is more memory\n",
+      "                             efficient.This chuck of variants will be process\n",
+      "                             and saved to output parquet file before contineus\n",
+      "                             to next chunk. Default: 10_000.\n",
+      "  --model TEXT               `0`, `1`, `2`, `3`, `ensemble` or a path or a\n",
+      "                             comma-separated list of paths to safetensor files\n",
+      "                             to perform variant effect prediction. Default:\n",
+      "                             `ensemble`.\n",
+      "  --metadata TEXT            Path to the metadata anndata file or name of the\n",
+      "                             model. If not provided, the compabilite metadata\n",
+      "                             for the model will be used. Default: ensemble.\n",
+      "  --device TEXT              Device to use. Default: None which automatically\n",
+      "                             selects the best device.\n",
+      "  --batch-size INTEGER       Batch size for the model. Default: 8\n",
+      "  --num-workers INTEGER      Number of workers for the loader. Default: 4\n",
+      "  --distance-type TEXT       Type of distance. Default: tss.\n",
+      "  --min-distance FLOAT       Minimum distance from the end of the gene.\n",
+      "                             Default: 0.\n",
+      "  --max-distance FLOAT       Maximum distance from the TSS. Default: 524288.\n",
+      "  --include-cols TEXT        Columns to include in the output in the original\n",
+      "                             tsv file to include in the output parquet file.\n",
+      "                             Default: None.\n",
+      "  --gene-col TEXT            Column name for gene names. Default: None.\n",
+      "  --genome TEXT              Genome build. Default: hg38.\n",
+      "  --save-replicates          Save the replicates in the output parquet file.\n",
+      "                             Default: False. Only supported for ensemble\n",
+      "                             models.\n",
+      "  --disable-reference-cache  Disables the reference cache which significantly\n",
+      "                             speeds up the computation by caching the\n",
+      "                             reference expression predictios in the metadata.\n",
+      "  --float-precision TEXT     Floating-point precision to be used in\n",
+      "                             calculations. Avaliable options include:\n",
+      "                             '16-true', '16-mixed', 'bf16-true', 'bf16-mixed',\n",
+      "                             '32-true', '64-true', '32', '16', and 'bf16'.\n",
+      "  --help                     Show this message and exit.\n",
       "\u001b[0m"
      ]
     }
@@ -131,9 +164,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "decima - INFO - Performing predictions on VariantDataset(48 variants from ['chr1'] between 516455 and 110234512 bp from TSS)\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima vep -v data/sample ...\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 96/96 \u001b[2m0:00:48 • 0:00:00\u001b[0m \u001b[2;4m1.77it/s\u001b[0m it/s\u001b[0m it/s\u001b[0m \n",
+      "\u001b[?25hdecima - WARNING - Warnings:\n",
+      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file /home/lala8/.local/share/genomes/hg38/hg38.fa. If this is not expected, please check if you are using the correct genome version.\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
    "source": [
     "! decima vep -v \"data/sample.vcf\" -o \"vep_vcf_results.parquet\""
    ]
@@ -148,12 +200,12 @@
      "output_type": "stream",
      "text": [
       "unknown: 0 / 48 \n",
-      "allele_mismatch_with_reference_genome: 26 / 48 \n"
+      "allele_mismatch_with_reference_genome: 10 / 48 \n"
      ]
     }
    ],
    "source": [
-    "! cat vep_vcf_results.parquet.warnings.log"
+    "! cat vep_vcf_results.warnings.log"
    ]
   },
   {
@@ -219,16 +271,16 @@
        "      <td>163840</td>\n",
        "      <td>172672</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000053</td>\n",
-       "      <td>-0.000153</td>\n",
-       "      <td>-0.000089</td>\n",
-       "      <td>-0.000016</td>\n",
-       "      <td>-0.000013</td>\n",
-       "      <td>-0.000040</td>\n",
-       "      <td>-0.000070</td>\n",
-       "      <td>7.629395e-05</td>\n",
-       "      <td>-0.000026</td>\n",
-       "      <td>-0.000069</td>\n",
+       "      <td>-0.038816</td>\n",
+       "      <td>-0.132628</td>\n",
+       "      <td>0.012945</td>\n",
+       "      <td>-0.057034</td>\n",
+       "      <td>-0.045095</td>\n",
+       "      <td>-0.019845</td>\n",
+       "      <td>-0.048861</td>\n",
+       "      <td>-0.248287</td>\n",
+       "      <td>-0.157361</td>\n",
+       "      <td>-0.202191</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -243,16 +295,16 @@
        "      <td>163840</td>\n",
        "      <td>178946</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000586</td>\n",
-       "      <td>-0.000891</td>\n",
-       "      <td>-0.000565</td>\n",
-       "      <td>-0.000311</td>\n",
-       "      <td>-0.000461</td>\n",
-       "      <td>-0.000252</td>\n",
-       "      <td>-0.000376</td>\n",
-       "      <td>-6.060898e-04</td>\n",
-       "      <td>-0.000454</td>\n",
-       "      <td>-0.000725</td>\n",
+       "      <td>0.007422</td>\n",
+       "      <td>-0.040294</td>\n",
+       "      <td>0.037184</td>\n",
+       "      <td>-0.039102</td>\n",
+       "      <td>-0.069273</td>\n",
+       "      <td>-0.089758</td>\n",
+       "      <td>0.085668</td>\n",
+       "      <td>-0.100051</td>\n",
+       "      <td>0.199948</td>\n",
+       "      <td>-0.228135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -267,16 +319,16 @@
        "      <td>163840</td>\n",
        "      <td>170729</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000565</td>\n",
-       "      <td>-0.000787</td>\n",
-       "      <td>-0.000515</td>\n",
-       "      <td>-0.000279</td>\n",
-       "      <td>-0.000354</td>\n",
-       "      <td>-0.000202</td>\n",
-       "      <td>-0.000342</td>\n",
-       "      <td>-5.399883e-04</td>\n",
-       "      <td>-0.000423</td>\n",
-       "      <td>-0.000597</td>\n",
+       "      <td>-0.039081</td>\n",
+       "      <td>-0.132857</td>\n",
+       "      <td>0.012576</td>\n",
+       "      <td>-0.057286</td>\n",
+       "      <td>-0.044211</td>\n",
+       "      <td>-0.019335</td>\n",
+       "      <td>-0.049245</td>\n",
+       "      <td>-0.247860</td>\n",
+       "      <td>-0.158028</td>\n",
+       "      <td>-0.202599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -291,16 +343,16 @@
        "      <td>163840</td>\n",
        "      <td>165050</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.001453</td>\n",
-       "      <td>-0.001775</td>\n",
-       "      <td>-0.001403</td>\n",
-       "      <td>-0.000820</td>\n",
-       "      <td>-0.000896</td>\n",
-       "      <td>-0.000575</td>\n",
-       "      <td>-0.001113</td>\n",
-       "      <td>-1.119316e-03</td>\n",
-       "      <td>-0.001036</td>\n",
-       "      <td>-0.001202</td>\n",
+       "      <td>0.007090</td>\n",
+       "      <td>-0.040752</td>\n",
+       "      <td>0.036865</td>\n",
+       "      <td>-0.038709</td>\n",
+       "      <td>-0.069798</td>\n",
+       "      <td>-0.090022</td>\n",
+       "      <td>0.085745</td>\n",
+       "      <td>-0.100093</td>\n",
+       "      <td>0.199475</td>\n",
+       "      <td>-0.228633</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -315,16 +367,16 @@
        "      <td>163840</td>\n",
        "      <td>166306</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000045</td>\n",
-       "      <td>0.000105</td>\n",
-       "      <td>0.000060</td>\n",
-       "      <td>-0.000030</td>\n",
-       "      <td>-0.000049</td>\n",
-       "      <td>0.000029</td>\n",
-       "      <td>0.000135</td>\n",
-       "      <td>1.490116e-07</td>\n",
-       "      <td>0.000057</td>\n",
-       "      <td>0.000001</td>\n",
+       "      <td>0.384038</td>\n",
+       "      <td>0.383892</td>\n",
+       "      <td>0.209059</td>\n",
+       "      <td>0.197611</td>\n",
+       "      <td>0.147189</td>\n",
+       "      <td>0.214235</td>\n",
+       "      <td>0.429188</td>\n",
+       "      <td>0.046692</td>\n",
+       "      <td>0.232973</td>\n",
+       "      <td>0.122444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -339,16 +391,16 @@
        "      <td>163840</td>\n",
        "      <td>167179</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000561</td>\n",
-       "      <td>-0.000826</td>\n",
-       "      <td>-0.000536</td>\n",
-       "      <td>-0.000299</td>\n",
-       "      <td>-0.000324</td>\n",
-       "      <td>-0.000196</td>\n",
-       "      <td>-0.000374</td>\n",
-       "      <td>-3.589988e-04</td>\n",
-       "      <td>-0.000342</td>\n",
-       "      <td>-0.000426</td>\n",
+       "      <td>0.128468</td>\n",
+       "      <td>0.201122</td>\n",
+       "      <td>0.096130</td>\n",
+       "      <td>0.117577</td>\n",
+       "      <td>-0.126177</td>\n",
+       "      <td>-0.059776</td>\n",
+       "      <td>0.184826</td>\n",
+       "      <td>-0.009697</td>\n",
+       "      <td>0.305417</td>\n",
+       "      <td>-0.107728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -363,16 +415,16 @@
        "      <td>163840</td>\n",
        "      <td>198383</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000399</td>\n",
-       "      <td>-0.000585</td>\n",
-       "      <td>-0.000374</td>\n",
-       "      <td>-0.000208</td>\n",
-       "      <td>-0.000212</td>\n",
-       "      <td>-0.000126</td>\n",
-       "      <td>-0.000265</td>\n",
-       "      <td>-2.926290e-04</td>\n",
-       "      <td>-0.000266</td>\n",
-       "      <td>-0.000376</td>\n",
+       "      <td>0.382609</td>\n",
+       "      <td>0.387782</td>\n",
+       "      <td>0.206949</td>\n",
+       "      <td>0.192670</td>\n",
+       "      <td>0.148521</td>\n",
+       "      <td>0.211755</td>\n",
+       "      <td>0.428720</td>\n",
+       "      <td>0.039945</td>\n",
+       "      <td>0.232015</td>\n",
+       "      <td>0.117866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -387,16 +439,16 @@
        "      <td>163840</td>\n",
        "      <td>184493</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001317</td>\n",
-       "      <td>0.001001</td>\n",
-       "      <td>0.000884</td>\n",
-       "      <td>0.001013</td>\n",
-       "      <td>0.000691</td>\n",
-       "      <td>0.001310</td>\n",
-       "      <td>0.001097</td>\n",
-       "      <td>5.598068e-04</td>\n",
-       "      <td>0.000501</td>\n",
-       "      <td>0.000827</td>\n",
+       "      <td>0.127029</td>\n",
+       "      <td>0.199535</td>\n",
+       "      <td>0.092907</td>\n",
+       "      <td>0.114106</td>\n",
+       "      <td>-0.132683</td>\n",
+       "      <td>-0.060256</td>\n",
+       "      <td>0.180594</td>\n",
+       "      <td>-0.008448</td>\n",
+       "      <td>0.303062</td>\n",
+       "      <td>-0.112591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -411,16 +463,16 @@
        "      <td>163840</td>\n",
        "      <td>168975</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000244</td>\n",
-       "      <td>-0.000214</td>\n",
-       "      <td>-0.000310</td>\n",
-       "      <td>-0.000153</td>\n",
-       "      <td>-0.000324</td>\n",
-       "      <td>-0.000168</td>\n",
-       "      <td>-0.000147</td>\n",
-       "      <td>-4.823208e-04</td>\n",
-       "      <td>-0.000211</td>\n",
-       "      <td>-0.000401</td>\n",
+       "      <td>-0.065035</td>\n",
+       "      <td>-0.009808</td>\n",
+       "      <td>-0.089304</td>\n",
+       "      <td>-0.123990</td>\n",
+       "      <td>-0.237687</td>\n",
+       "      <td>-0.145043</td>\n",
+       "      <td>-0.027265</td>\n",
+       "      <td>-0.172405</td>\n",
+       "      <td>-0.209000</td>\n",
+       "      <td>-0.169588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -435,16 +487,16 @@
        "      <td>163840</td>\n",
        "      <td>173223</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>-0.000113</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>-0.000028</td>\n",
-       "      <td>-0.000248</td>\n",
-       "      <td>-0.000283</td>\n",
-       "      <td>-0.000314</td>\n",
-       "      <td>-1.890063e-04</td>\n",
-       "      <td>-0.000022</td>\n",
-       "      <td>-0.000029</td>\n",
+       "      <td>0.149715</td>\n",
+       "      <td>0.036652</td>\n",
+       "      <td>0.236477</td>\n",
+       "      <td>-0.033082</td>\n",
+       "      <td>-0.059123</td>\n",
+       "      <td>-0.109544</td>\n",
+       "      <td>-0.116550</td>\n",
+       "      <td>-0.056748</td>\n",
+       "      <td>0.238428</td>\n",
+       "      <td>-0.066090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -459,16 +511,16 @@
        "      <td>163840</td>\n",
        "      <td>170339</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000286</td>\n",
-       "      <td>-0.000486</td>\n",
-       "      <td>-0.000312</td>\n",
-       "      <td>-0.000150</td>\n",
-       "      <td>-0.000256</td>\n",
-       "      <td>-0.000124</td>\n",
-       "      <td>-0.000179</td>\n",
-       "      <td>-3.057718e-04</td>\n",
-       "      <td>-0.000183</td>\n",
-       "      <td>-0.000252</td>\n",
+       "      <td>-0.064055</td>\n",
+       "      <td>-0.008839</td>\n",
+       "      <td>-0.088600</td>\n",
+       "      <td>-0.123287</td>\n",
+       "      <td>-0.236878</td>\n",
+       "      <td>-0.144462</td>\n",
+       "      <td>-0.026132</td>\n",
+       "      <td>-0.171725</td>\n",
+       "      <td>-0.208052</td>\n",
+       "      <td>-0.169746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -483,16 +535,16 @@
        "      <td>163840</td>\n",
        "      <td>177242</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.007558</td>\n",
-       "      <td>0.004934</td>\n",
-       "      <td>0.007966</td>\n",
-       "      <td>0.007595</td>\n",
-       "      <td>-0.001943</td>\n",
-       "      <td>0.001598</td>\n",
-       "      <td>0.007838</td>\n",
-       "      <td>2.528191e-03</td>\n",
-       "      <td>0.007137</td>\n",
-       "      <td>0.009534</td>\n",
+       "      <td>0.148965</td>\n",
+       "      <td>0.036048</td>\n",
+       "      <td>0.236015</td>\n",
+       "      <td>-0.033107</td>\n",
+       "      <td>-0.059445</td>\n",
+       "      <td>-0.110096</td>\n",
+       "      <td>-0.117363</td>\n",
+       "      <td>-0.057286</td>\n",
+       "      <td>0.237902</td>\n",
+       "      <td>-0.067406</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
@@ -507,16 +559,16 @@
        "      <td>163840</td>\n",
        "      <td>166924</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000190</td>\n",
-       "      <td>-0.000248</td>\n",
-       "      <td>-0.000215</td>\n",
-       "      <td>-0.000113</td>\n",
-       "      <td>-0.000070</td>\n",
-       "      <td>-0.000076</td>\n",
-       "      <td>-0.000159</td>\n",
-       "      <td>-5.897880e-05</td>\n",
-       "      <td>-0.000117</td>\n",
-       "      <td>-0.000182</td>\n",
+       "      <td>0.025167</td>\n",
+       "      <td>0.316047</td>\n",
+       "      <td>0.052610</td>\n",
+       "      <td>-0.128209</td>\n",
+       "      <td>-0.025701</td>\n",
+       "      <td>0.018469</td>\n",
+       "      <td>0.071030</td>\n",
+       "      <td>0.035337</td>\n",
+       "      <td>0.035106</td>\n",
+       "      <td>0.198629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -531,16 +583,16 @@
        "      <td>163840</td>\n",
        "      <td>166653</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000217</td>\n",
-       "      <td>-0.000332</td>\n",
-       "      <td>-0.000225</td>\n",
-       "      <td>-0.000132</td>\n",
-       "      <td>-0.000127</td>\n",
-       "      <td>-0.000102</td>\n",
-       "      <td>-0.000146</td>\n",
-       "      <td>-2.127588e-04</td>\n",
-       "      <td>-0.000168</td>\n",
-       "      <td>-0.000276</td>\n",
+       "      <td>-0.060978</td>\n",
+       "      <td>0.136151</td>\n",
+       "      <td>0.222250</td>\n",
+       "      <td>0.180415</td>\n",
+       "      <td>-0.077709</td>\n",
+       "      <td>0.059230</td>\n",
+       "      <td>-0.152584</td>\n",
+       "      <td>0.021212</td>\n",
+       "      <td>0.387905</td>\n",
+       "      <td>0.087836</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
@@ -555,16 +607,16 @@
        "      <td>163840</td>\n",
        "      <td>199838</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000643</td>\n",
-       "      <td>-0.000886</td>\n",
-       "      <td>-0.000513</td>\n",
-       "      <td>-0.001688</td>\n",
-       "      <td>-0.000346</td>\n",
-       "      <td>-0.000528</td>\n",
-       "      <td>-0.000894</td>\n",
-       "      <td>-4.293919e-04</td>\n",
-       "      <td>-0.001309</td>\n",
-       "      <td>-0.000301</td>\n",
+       "      <td>0.024651</td>\n",
+       "      <td>0.315260</td>\n",
+       "      <td>0.050833</td>\n",
+       "      <td>-0.121852</td>\n",
+       "      <td>-0.024862</td>\n",
+       "      <td>0.020706</td>\n",
+       "      <td>0.065433</td>\n",
+       "      <td>0.039559</td>\n",
+       "      <td>0.038879</td>\n",
+       "      <td>0.207624</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
@@ -579,16 +631,16 @@
        "      <td>163840</td>\n",
        "      <td>178999</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000031</td>\n",
-       "      <td>-0.000034</td>\n",
-       "      <td>-0.000048</td>\n",
-       "      <td>-0.000017</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>-0.000002</td>\n",
-       "      <td>-0.000036</td>\n",
-       "      <td>-1.719594e-05</td>\n",
-       "      <td>-0.000026</td>\n",
-       "      <td>-0.000037</td>\n",
+       "      <td>-0.069684</td>\n",
+       "      <td>0.130147</td>\n",
+       "      <td>0.214494</td>\n",
+       "      <td>0.163851</td>\n",
+       "      <td>-0.088984</td>\n",
+       "      <td>0.049194</td>\n",
+       "      <td>-0.161672</td>\n",
+       "      <td>0.012433</td>\n",
+       "      <td>0.376862</td>\n",
+       "      <td>0.079508</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
@@ -603,16 +655,16 @@
        "      <td>163840</td>\n",
        "      <td>168116</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000396</td>\n",
-       "      <td>-0.000607</td>\n",
-       "      <td>-0.000421</td>\n",
-       "      <td>-0.000306</td>\n",
-       "      <td>-0.000294</td>\n",
-       "      <td>-0.000207</td>\n",
-       "      <td>-0.000237</td>\n",
-       "      <td>-3.867447e-04</td>\n",
-       "      <td>-0.000289</td>\n",
-       "      <td>-0.000498</td>\n",
+       "      <td>-0.068721</td>\n",
+       "      <td>-0.131200</td>\n",
+       "      <td>-0.095311</td>\n",
+       "      <td>0.046062</td>\n",
+       "      <td>0.058227</td>\n",
+       "      <td>-0.014569</td>\n",
+       "      <td>-0.110806</td>\n",
+       "      <td>0.156508</td>\n",
+       "      <td>0.050760</td>\n",
+       "      <td>0.086102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
@@ -627,16 +679,16 @@
        "      <td>163840</td>\n",
        "      <td>183816</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000809</td>\n",
-       "      <td>-0.001129</td>\n",
-       "      <td>-0.000884</td>\n",
-       "      <td>-0.000664</td>\n",
-       "      <td>-0.000741</td>\n",
-       "      <td>-0.000528</td>\n",
-       "      <td>-0.000512</td>\n",
-       "      <td>-7.908940e-04</td>\n",
-       "      <td>-0.000607</td>\n",
-       "      <td>-0.000990</td>\n",
+       "      <td>-0.000722</td>\n",
+       "      <td>-0.065477</td>\n",
+       "      <td>-0.015448</td>\n",
+       "      <td>-0.048512</td>\n",
+       "      <td>0.080478</td>\n",
+       "      <td>0.044108</td>\n",
+       "      <td>-0.040190</td>\n",
+       "      <td>-0.109651</td>\n",
+       "      <td>0.016822</td>\n",
+       "      <td>-0.062620</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
@@ -651,16 +703,16 @@
        "      <td>163840</td>\n",
        "      <td>181059</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000826</td>\n",
-       "      <td>-0.001292</td>\n",
-       "      <td>-0.000932</td>\n",
-       "      <td>-0.000710</td>\n",
-       "      <td>-0.000789</td>\n",
-       "      <td>-0.000506</td>\n",
-       "      <td>-0.000457</td>\n",
-       "      <td>-9.316206e-04</td>\n",
-       "      <td>-0.000666</td>\n",
-       "      <td>-0.001122</td>\n",
+       "      <td>-0.068239</td>\n",
+       "      <td>-0.130473</td>\n",
+       "      <td>-0.095077</td>\n",
+       "      <td>0.046099</td>\n",
+       "      <td>0.058977</td>\n",
+       "      <td>-0.014226</td>\n",
+       "      <td>-0.110129</td>\n",
+       "      <td>0.156933</td>\n",
+       "      <td>0.051034</td>\n",
+       "      <td>0.086115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
@@ -675,16 +727,16 @@
        "      <td>163840</td>\n",
        "      <td>176946</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000033</td>\n",
-       "      <td>-0.000067</td>\n",
-       "      <td>-0.000013</td>\n",
-       "      <td>-0.000010</td>\n",
-       "      <td>0.000030</td>\n",
-       "      <td>0.000017</td>\n",
-       "      <td>-0.000020</td>\n",
-       "      <td>1.147389e-04</td>\n",
-       "      <td>-0.000005</td>\n",
-       "      <td>-0.000017</td>\n",
+       "      <td>-0.000799</td>\n",
+       "      <td>-0.065457</td>\n",
+       "      <td>-0.015547</td>\n",
+       "      <td>-0.048479</td>\n",
+       "      <td>0.080698</td>\n",
+       "      <td>0.044615</td>\n",
+       "      <td>-0.040375</td>\n",
+       "      <td>-0.109146</td>\n",
+       "      <td>0.016773</td>\n",
+       "      <td>-0.063049</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
@@ -699,16 +751,16 @@
        "      <td>163840</td>\n",
        "      <td>177982</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000297</td>\n",
-       "      <td>-0.000456</td>\n",
-       "      <td>-0.000343</td>\n",
-       "      <td>-0.000253</td>\n",
-       "      <td>-0.000310</td>\n",
-       "      <td>-0.000204</td>\n",
-       "      <td>-0.000172</td>\n",
-       "      <td>-3.470182e-04</td>\n",
-       "      <td>-0.000220</td>\n",
-       "      <td>-0.000371</td>\n",
+       "      <td>0.144724</td>\n",
+       "      <td>0.096923</td>\n",
+       "      <td>0.053529</td>\n",
+       "      <td>0.178255</td>\n",
+       "      <td>0.186835</td>\n",
+       "      <td>-0.086366</td>\n",
+       "      <td>0.078235</td>\n",
+       "      <td>0.339634</td>\n",
+       "      <td>0.164180</td>\n",
+       "      <td>0.351917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
@@ -723,16 +775,16 @@
        "      <td>163840</td>\n",
        "      <td>172928</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000118</td>\n",
-       "      <td>-0.000153</td>\n",
-       "      <td>-0.000130</td>\n",
-       "      <td>-0.000090</td>\n",
-       "      <td>-0.000128</td>\n",
-       "      <td>-0.000076</td>\n",
-       "      <td>-0.000053</td>\n",
-       "      <td>-2.347827e-04</td>\n",
-       "      <td>-0.000090</td>\n",
-       "      <td>-0.000214</td>\n",
+       "      <td>-0.204588</td>\n",
+       "      <td>-0.164875</td>\n",
+       "      <td>-0.161451</td>\n",
+       "      <td>-0.077614</td>\n",
+       "      <td>0.001604</td>\n",
+       "      <td>-0.079316</td>\n",
+       "      <td>-0.283131</td>\n",
+       "      <td>-0.060867</td>\n",
+       "      <td>-0.083952</td>\n",
+       "      <td>-0.061946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
@@ -747,16 +799,16 @@
        "      <td>163840</td>\n",
        "      <td>180515</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000099</td>\n",
-       "      <td>0.000077</td>\n",
-       "      <td>0.000057</td>\n",
-       "      <td>0.000040</td>\n",
-       "      <td>0.000018</td>\n",
-       "      <td>0.000047</td>\n",
-       "      <td>0.000075</td>\n",
-       "      <td>2.750456e-04</td>\n",
-       "      <td>0.000065</td>\n",
-       "      <td>0.000258</td>\n",
+       "      <td>0.145371</td>\n",
+       "      <td>0.099419</td>\n",
+       "      <td>0.055633</td>\n",
+       "      <td>0.177352</td>\n",
+       "      <td>0.190575</td>\n",
+       "      <td>-0.084158</td>\n",
+       "      <td>0.083270</td>\n",
+       "      <td>0.344398</td>\n",
+       "      <td>0.166967</td>\n",
+       "      <td>0.355001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
@@ -771,16 +823,16 @@
        "      <td>163840</td>\n",
        "      <td>180515</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.003284</td>\n",
-       "      <td>0.005105</td>\n",
-       "      <td>0.002662</td>\n",
-       "      <td>0.001423</td>\n",
-       "      <td>0.001525</td>\n",
-       "      <td>0.001257</td>\n",
-       "      <td>0.001919</td>\n",
-       "      <td>5.420089e-03</td>\n",
-       "      <td>0.002166</td>\n",
-       "      <td>0.005263</td>\n",
+       "      <td>-0.216764</td>\n",
+       "      <td>-0.175109</td>\n",
+       "      <td>-0.172126</td>\n",
+       "      <td>-0.086336</td>\n",
+       "      <td>-0.018204</td>\n",
+       "      <td>-0.098414</td>\n",
+       "      <td>-0.295143</td>\n",
+       "      <td>-0.081368</td>\n",
+       "      <td>-0.096516</td>\n",
+       "      <td>-0.078732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
@@ -795,16 +847,16 @@
        "      <td>163840</td>\n",
        "      <td>179428</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.002200</td>\n",
-       "      <td>0.001744</td>\n",
-       "      <td>0.001206</td>\n",
-       "      <td>0.001263</td>\n",
-       "      <td>0.001008</td>\n",
-       "      <td>-0.000117</td>\n",
-       "      <td>0.001218</td>\n",
-       "      <td>2.799034e-04</td>\n",
-       "      <td>0.001503</td>\n",
-       "      <td>0.001372</td>\n",
+       "      <td>0.125082</td>\n",
+       "      <td>0.187445</td>\n",
+       "      <td>0.094098</td>\n",
+       "      <td>0.073541</td>\n",
+       "      <td>0.153524</td>\n",
+       "      <td>0.122184</td>\n",
+       "      <td>0.178186</td>\n",
+       "      <td>0.325020</td>\n",
+       "      <td>0.131711</td>\n",
+       "      <td>0.251710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
@@ -819,16 +871,16 @@
        "      <td>163840</td>\n",
        "      <td>179428</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.004052</td>\n",
-       "      <td>-0.003465</td>\n",
-       "      <td>-0.003092</td>\n",
-       "      <td>-0.003744</td>\n",
-       "      <td>-0.003384</td>\n",
-       "      <td>-0.003265</td>\n",
-       "      <td>-0.003253</td>\n",
-       "      <td>1.600981e-03</td>\n",
-       "      <td>-0.001915</td>\n",
-       "      <td>-0.002728</td>\n",
+       "      <td>-0.072443</td>\n",
+       "      <td>-0.081550</td>\n",
+       "      <td>-0.038486</td>\n",
+       "      <td>-0.047714</td>\n",
+       "      <td>0.226176</td>\n",
+       "      <td>0.045261</td>\n",
+       "      <td>-0.126760</td>\n",
+       "      <td>0.371777</td>\n",
+       "      <td>0.060598</td>\n",
+       "      <td>-0.005164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
@@ -843,16 +895,16 @@
        "      <td>163840</td>\n",
        "      <td>173165</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>0.000010</td>\n",
-       "      <td>0.000091</td>\n",
-       "      <td>-0.000079</td>\n",
-       "      <td>-0.000044</td>\n",
-       "      <td>-0.000061</td>\n",
-       "      <td>-0.000042</td>\n",
-       "      <td>-4.082918e-05</td>\n",
-       "      <td>-0.000022</td>\n",
-       "      <td>-0.000060</td>\n",
+       "      <td>0.125229</td>\n",
+       "      <td>0.187538</td>\n",
+       "      <td>0.094372</td>\n",
+       "      <td>0.073884</td>\n",
+       "      <td>0.154733</td>\n",
+       "      <td>0.122949</td>\n",
+       "      <td>0.178414</td>\n",
+       "      <td>0.326226</td>\n",
+       "      <td>0.132067</td>\n",
+       "      <td>0.251894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
@@ -867,16 +919,16 @@
        "      <td>163840</td>\n",
        "      <td>173165</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000536</td>\n",
-       "      <td>-0.000179</td>\n",
-       "      <td>0.001585</td>\n",
-       "      <td>0.001431</td>\n",
-       "      <td>0.003103</td>\n",
-       "      <td>0.001631</td>\n",
-       "      <td>0.000257</td>\n",
-       "      <td>2.081454e-03</td>\n",
-       "      <td>0.000913</td>\n",
-       "      <td>0.001677</td>\n",
+       "      <td>-0.072734</td>\n",
+       "      <td>-0.082032</td>\n",
+       "      <td>-0.038806</td>\n",
+       "      <td>-0.047959</td>\n",
+       "      <td>0.226084</td>\n",
+       "      <td>0.045132</td>\n",
+       "      <td>-0.126927</td>\n",
+       "      <td>0.371727</td>\n",
+       "      <td>0.060446</td>\n",
+       "      <td>-0.005538</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
@@ -891,16 +943,16 @@
        "      <td>163840</td>\n",
        "      <td>172720</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000584</td>\n",
-       "      <td>0.000577</td>\n",
-       "      <td>0.000674</td>\n",
-       "      <td>0.000536</td>\n",
-       "      <td>0.000167</td>\n",
-       "      <td>0.000397</td>\n",
-       "      <td>0.000507</td>\n",
-       "      <td>1.705885e-04</td>\n",
-       "      <td>0.000503</td>\n",
-       "      <td>0.000481</td>\n",
+       "      <td>-0.109239</td>\n",
+       "      <td>-0.095734</td>\n",
+       "      <td>-0.046736</td>\n",
+       "      <td>-0.160156</td>\n",
+       "      <td>-0.108054</td>\n",
+       "      <td>-0.116374</td>\n",
+       "      <td>0.049736</td>\n",
+       "      <td>0.170926</td>\n",
+       "      <td>0.112000</td>\n",
+       "      <td>0.250690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
@@ -915,16 +967,16 @@
        "      <td>163840</td>\n",
        "      <td>172720</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.003762</td>\n",
-       "      <td>0.002849</td>\n",
-       "      <td>0.001487</td>\n",
-       "      <td>0.002733</td>\n",
-       "      <td>-0.000099</td>\n",
-       "      <td>0.000631</td>\n",
-       "      <td>0.001469</td>\n",
-       "      <td>4.820824e-04</td>\n",
-       "      <td>0.002098</td>\n",
-       "      <td>-0.000244</td>\n",
+       "      <td>-0.014338</td>\n",
+       "      <td>-0.047306</td>\n",
+       "      <td>0.040593</td>\n",
+       "      <td>0.034276</td>\n",
+       "      <td>0.086477</td>\n",
+       "      <td>-0.002776</td>\n",
+       "      <td>-0.036525</td>\n",
+       "      <td>0.105931</td>\n",
+       "      <td>0.075943</td>\n",
+       "      <td>-0.094462</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
@@ -939,16 +991,16 @@
        "      <td>163840</td>\n",
        "      <td>172374</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000155</td>\n",
-       "      <td>0.000251</td>\n",
-       "      <td>0.000114</td>\n",
-       "      <td>0.000134</td>\n",
-       "      <td>-0.000137</td>\n",
-       "      <td>-0.000068</td>\n",
-       "      <td>0.000125</td>\n",
-       "      <td>4.252791e-05</td>\n",
-       "      <td>0.000117</td>\n",
-       "      <td>0.000137</td>\n",
+       "      <td>-0.114962</td>\n",
+       "      <td>-0.101682</td>\n",
+       "      <td>-0.051288</td>\n",
+       "      <td>-0.167562</td>\n",
+       "      <td>-0.114795</td>\n",
+       "      <td>-0.118255</td>\n",
+       "      <td>0.045381</td>\n",
+       "      <td>0.164219</td>\n",
+       "      <td>0.106147</td>\n",
+       "      <td>0.242618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
@@ -963,16 +1015,16 @@
        "      <td>163840</td>\n",
        "      <td>172374</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000258</td>\n",
-       "      <td>0.000810</td>\n",
-       "      <td>0.000308</td>\n",
-       "      <td>0.000489</td>\n",
-       "      <td>0.000618</td>\n",
-       "      <td>0.000236</td>\n",
-       "      <td>0.000523</td>\n",
-       "      <td>6.991625e-04</td>\n",
-       "      <td>0.000155</td>\n",
-       "      <td>0.000536</td>\n",
+       "      <td>-0.014739</td>\n",
+       "      <td>-0.051509</td>\n",
+       "      <td>0.039989</td>\n",
+       "      <td>0.034758</td>\n",
+       "      <td>0.078923</td>\n",
+       "      <td>-0.006194</td>\n",
+       "      <td>-0.037574</td>\n",
+       "      <td>0.102354</td>\n",
+       "      <td>0.074973</td>\n",
+       "      <td>-0.093299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
@@ -987,16 +1039,16 @@
        "      <td>163840</td>\n",
        "      <td>170946</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000651</td>\n",
-       "      <td>-0.000626</td>\n",
-       "      <td>-0.000446</td>\n",
-       "      <td>-0.000290</td>\n",
-       "      <td>-0.000174</td>\n",
-       "      <td>-0.000026</td>\n",
-       "      <td>-0.000154</td>\n",
-       "      <td>-3.945976e-04</td>\n",
-       "      <td>-0.000396</td>\n",
-       "      <td>-0.000499</td>\n",
+       "      <td>0.017597</td>\n",
+       "      <td>-0.080504</td>\n",
+       "      <td>0.049995</td>\n",
+       "      <td>-0.011808</td>\n",
+       "      <td>-0.271627</td>\n",
+       "      <td>-0.061634</td>\n",
+       "      <td>-0.068092</td>\n",
+       "      <td>-0.169892</td>\n",
+       "      <td>-0.139209</td>\n",
+       "      <td>-0.134266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
@@ -1011,16 +1063,16 @@
        "      <td>163840</td>\n",
        "      <td>170946</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.014822</td>\n",
-       "      <td>0.029157</td>\n",
-       "      <td>0.015316</td>\n",
-       "      <td>0.007980</td>\n",
-       "      <td>0.015211</td>\n",
-       "      <td>0.007929</td>\n",
-       "      <td>0.010251</td>\n",
-       "      <td>2.181430e-02</td>\n",
-       "      <td>0.009214</td>\n",
-       "      <td>0.024373</td>\n",
+       "      <td>-0.297318</td>\n",
+       "      <td>0.036640</td>\n",
+       "      <td>0.039409</td>\n",
+       "      <td>0.172793</td>\n",
+       "      <td>0.131960</td>\n",
+       "      <td>0.099102</td>\n",
+       "      <td>-0.127269</td>\n",
+       "      <td>0.248293</td>\n",
+       "      <td>0.078142</td>\n",
+       "      <td>0.244484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
@@ -1035,16 +1087,16 @@
        "      <td>163840</td>\n",
        "      <td>233549</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.002052</td>\n",
-       "      <td>0.002637</td>\n",
-       "      <td>0.003581</td>\n",
-       "      <td>0.001084</td>\n",
-       "      <td>-0.001867</td>\n",
-       "      <td>-0.003804</td>\n",
-       "      <td>0.003472</td>\n",
-       "      <td>-1.005411e-03</td>\n",
-       "      <td>0.003400</td>\n",
-       "      <td>0.001397</td>\n",
+       "      <td>0.016951</td>\n",
+       "      <td>-0.081113</td>\n",
+       "      <td>0.048497</td>\n",
+       "      <td>-0.012860</td>\n",
+       "      <td>-0.272076</td>\n",
+       "      <td>-0.061712</td>\n",
+       "      <td>-0.068612</td>\n",
+       "      <td>-0.168814</td>\n",
+       "      <td>-0.140736</td>\n",
+       "      <td>-0.135443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
@@ -1059,16 +1111,16 @@
        "      <td>163840</td>\n",
        "      <td>233549</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.002514</td>\n",
-       "      <td>-0.002176</td>\n",
-       "      <td>0.001273</td>\n",
-       "      <td>0.007376</td>\n",
-       "      <td>0.011271</td>\n",
-       "      <td>0.019305</td>\n",
-       "      <td>-0.002886</td>\n",
-       "      <td>2.035785e-02</td>\n",
-       "      <td>-0.001660</td>\n",
-       "      <td>0.006990</td>\n",
+       "      <td>-0.296372</td>\n",
+       "      <td>0.026870</td>\n",
+       "      <td>0.034203</td>\n",
+       "      <td>0.171992</td>\n",
+       "      <td>0.132727</td>\n",
+       "      <td>0.100970</td>\n",
+       "      <td>-0.133759</td>\n",
+       "      <td>0.247039</td>\n",
+       "      <td>0.073710</td>\n",
+       "      <td>0.242920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
@@ -1083,16 +1135,16 @@
        "      <td>163840</td>\n",
        "      <td>179789</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000870</td>\n",
-       "      <td>0.001771</td>\n",
-       "      <td>0.002003</td>\n",
-       "      <td>0.000641</td>\n",
-       "      <td>-0.002239</td>\n",
-       "      <td>-0.000491</td>\n",
-       "      <td>0.001610</td>\n",
-       "      <td>-6.132126e-04</td>\n",
-       "      <td>0.002603</td>\n",
-       "      <td>0.000714</td>\n",
+       "      <td>0.172656</td>\n",
+       "      <td>0.238587</td>\n",
+       "      <td>-0.112662</td>\n",
+       "      <td>-0.397093</td>\n",
+       "      <td>-0.048434</td>\n",
+       "      <td>-0.349497</td>\n",
+       "      <td>0.178258</td>\n",
+       "      <td>-0.541721</td>\n",
+       "      <td>-0.432872</td>\n",
+       "      <td>-0.115762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
@@ -1107,16 +1159,16 @@
        "      <td>163840</td>\n",
        "      <td>179789</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.015270</td>\n",
-       "      <td>0.004848</td>\n",
-       "      <td>0.011452</td>\n",
-       "      <td>0.028524</td>\n",
-       "      <td>0.009958</td>\n",
-       "      <td>0.013799</td>\n",
-       "      <td>0.007597</td>\n",
-       "      <td>2.641273e-02</td>\n",
-       "      <td>0.009691</td>\n",
-       "      <td>0.026180</td>\n",
+       "      <td>-0.587357</td>\n",
+       "      <td>-0.383792</td>\n",
+       "      <td>-0.075256</td>\n",
+       "      <td>0.116061</td>\n",
+       "      <td>-0.107381</td>\n",
+       "      <td>-0.537354</td>\n",
+       "      <td>-0.290173</td>\n",
+       "      <td>0.274848</td>\n",
+       "      <td>-0.387343</td>\n",
+       "      <td>-0.475388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
@@ -1131,16 +1183,16 @@
        "      <td>163840</td>\n",
        "      <td>182577</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000336</td>\n",
-       "      <td>0.001399</td>\n",
-       "      <td>0.000511</td>\n",
-       "      <td>0.000450</td>\n",
-       "      <td>-0.000501</td>\n",
-       "      <td>-0.000243</td>\n",
-       "      <td>-0.001328</td>\n",
-       "      <td>4.479885e-04</td>\n",
-       "      <td>0.000544</td>\n",
-       "      <td>0.002848</td>\n",
+       "      <td>0.196620</td>\n",
+       "      <td>0.244671</td>\n",
+       "      <td>-0.095485</td>\n",
+       "      <td>-0.382966</td>\n",
+       "      <td>-0.053531</td>\n",
+       "      <td>-0.346830</td>\n",
+       "      <td>0.205384</td>\n",
+       "      <td>-0.547652</td>\n",
+       "      <td>-0.427548</td>\n",
+       "      <td>-0.109037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
@@ -1155,16 +1207,16 @@
        "      <td>163840</td>\n",
        "      <td>182577</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.032543</td>\n",
-       "      <td>-0.033684</td>\n",
-       "      <td>-0.029430</td>\n",
-       "      <td>-0.050647</td>\n",
-       "      <td>-0.030112</td>\n",
-       "      <td>-0.021851</td>\n",
-       "      <td>-0.012187</td>\n",
-       "      <td>-2.968431e-02</td>\n",
-       "      <td>-0.037312</td>\n",
-       "      <td>-0.067244</td>\n",
+       "      <td>-0.598168</td>\n",
+       "      <td>-0.350979</td>\n",
+       "      <td>-0.061809</td>\n",
+       "      <td>0.137094</td>\n",
+       "      <td>-0.114290</td>\n",
+       "      <td>-0.549722</td>\n",
+       "      <td>-0.316452</td>\n",
+       "      <td>0.281865</td>\n",
+       "      <td>-0.367328</td>\n",
+       "      <td>-0.457750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
@@ -1179,16 +1231,16 @@
        "      <td>163840</td>\n",
        "      <td>205369</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.001145</td>\n",
-       "      <td>-0.001371</td>\n",
-       "      <td>0.000107</td>\n",
-       "      <td>-0.000453</td>\n",
-       "      <td>-0.001597</td>\n",
-       "      <td>-0.000798</td>\n",
-       "      <td>-0.001882</td>\n",
-       "      <td>1.063347e-04</td>\n",
-       "      <td>0.000106</td>\n",
-       "      <td>0.000086</td>\n",
+       "      <td>-0.065015</td>\n",
+       "      <td>-0.134536</td>\n",
+       "      <td>-0.140023</td>\n",
+       "      <td>0.024319</td>\n",
+       "      <td>-0.060507</td>\n",
+       "      <td>-0.108153</td>\n",
+       "      <td>-0.149791</td>\n",
+       "      <td>-0.038775</td>\n",
+       "      <td>-0.076944</td>\n",
+       "      <td>-0.118178</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
@@ -1203,16 +1255,16 @@
        "      <td>163840</td>\n",
        "      <td>205369</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.006410</td>\n",
-       "      <td>0.010421</td>\n",
-       "      <td>0.008430</td>\n",
-       "      <td>0.010849</td>\n",
-       "      <td>-0.004994</td>\n",
-       "      <td>0.003090</td>\n",
-       "      <td>-0.004843</td>\n",
-       "      <td>-9.270430e-03</td>\n",
-       "      <td>0.008755</td>\n",
-       "      <td>0.011827</td>\n",
+       "      <td>-0.138112</td>\n",
+       "      <td>-0.135524</td>\n",
+       "      <td>-0.122887</td>\n",
+       "      <td>0.003460</td>\n",
+       "      <td>-0.095921</td>\n",
+       "      <td>-0.142851</td>\n",
+       "      <td>-0.133408</td>\n",
+       "      <td>-0.044298</td>\n",
+       "      <td>-0.079864</td>\n",
+       "      <td>0.063350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
@@ -1227,16 +1279,16 @@
        "      <td>163840</td>\n",
        "      <td>185065</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000849</td>\n",
-       "      <td>0.001993</td>\n",
-       "      <td>0.001033</td>\n",
-       "      <td>-0.000050</td>\n",
-       "      <td>-0.000534</td>\n",
-       "      <td>0.000095</td>\n",
-       "      <td>0.000409</td>\n",
-       "      <td>-3.244877e-04</td>\n",
-       "      <td>0.000842</td>\n",
-       "      <td>0.000388</td>\n",
+       "      <td>-0.064923</td>\n",
+       "      <td>-0.134493</td>\n",
+       "      <td>-0.140439</td>\n",
+       "      <td>0.024307</td>\n",
+       "      <td>-0.060060</td>\n",
+       "      <td>-0.106959</td>\n",
+       "      <td>-0.150438</td>\n",
+       "      <td>-0.038277</td>\n",
+       "      <td>-0.077278</td>\n",
+       "      <td>-0.118272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
@@ -1251,16 +1303,16 @@
        "      <td>163840</td>\n",
        "      <td>185065</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.009973</td>\n",
-       "      <td>0.012482</td>\n",
-       "      <td>0.003073</td>\n",
-       "      <td>0.019083</td>\n",
-       "      <td>0.010606</td>\n",
-       "      <td>0.000387</td>\n",
-       "      <td>0.003512</td>\n",
-       "      <td>7.757962e-03</td>\n",
-       "      <td>0.011860</td>\n",
-       "      <td>0.019928</td>\n",
+       "      <td>-0.138915</td>\n",
+       "      <td>-0.137064</td>\n",
+       "      <td>-0.123798</td>\n",
+       "      <td>0.002397</td>\n",
+       "      <td>-0.096466</td>\n",
+       "      <td>-0.143198</td>\n",
+       "      <td>-0.133723</td>\n",
+       "      <td>-0.044947</td>\n",
+       "      <td>-0.080987</td>\n",
+       "      <td>0.062265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
@@ -1275,16 +1327,16 @@
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.002434</td>\n",
-       "      <td>0.001946</td>\n",
-       "      <td>0.001824</td>\n",
-       "      <td>0.000374</td>\n",
-       "      <td>-0.000416</td>\n",
-       "      <td>-0.000871</td>\n",
-       "      <td>0.003142</td>\n",
-       "      <td>-1.690984e-03</td>\n",
-       "      <td>0.001557</td>\n",
-       "      <td>0.002596</td>\n",
+       "      <td>-0.260050</td>\n",
+       "      <td>-0.168407</td>\n",
+       "      <td>-0.271245</td>\n",
+       "      <td>-0.247335</td>\n",
+       "      <td>-0.234919</td>\n",
+       "      <td>-0.383042</td>\n",
+       "      <td>-0.163728</td>\n",
+       "      <td>-0.055309</td>\n",
+       "      <td>-0.088513</td>\n",
+       "      <td>-0.155641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
@@ -1299,16 +1351,16 @@
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.005434</td>\n",
-       "      <td>0.003420</td>\n",
-       "      <td>-0.019319</td>\n",
-       "      <td>-0.000747</td>\n",
-       "      <td>0.041849</td>\n",
-       "      <td>0.022034</td>\n",
-       "      <td>-0.024061</td>\n",
-       "      <td>4.308212e-02</td>\n",
-       "      <td>0.005673</td>\n",
-       "      <td>-0.007195</td>\n",
+       "      <td>0.107971</td>\n",
+       "      <td>0.020257</td>\n",
+       "      <td>0.216171</td>\n",
+       "      <td>0.572421</td>\n",
+       "      <td>0.147785</td>\n",
+       "      <td>0.106555</td>\n",
+       "      <td>0.096098</td>\n",
+       "      <td>0.279161</td>\n",
+       "      <td>0.162524</td>\n",
+       "      <td>0.332554</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
@@ -1323,16 +1375,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000210</td>\n",
-       "      <td>0.000521</td>\n",
-       "      <td>0.000256</td>\n",
-       "      <td>0.000118</td>\n",
-       "      <td>0.000335</td>\n",
-       "      <td>0.000174</td>\n",
-       "      <td>0.000136</td>\n",
-       "      <td>8.502305e-04</td>\n",
-       "      <td>0.000165</td>\n",
-       "      <td>0.000544</td>\n",
+       "      <td>-0.256655</td>\n",
+       "      <td>-0.163457</td>\n",
+       "      <td>-0.266899</td>\n",
+       "      <td>-0.247330</td>\n",
+       "      <td>-0.232560</td>\n",
+       "      <td>-0.386224</td>\n",
+       "      <td>-0.155104</td>\n",
+       "      <td>-0.059262</td>\n",
+       "      <td>-0.081452</td>\n",
+       "      <td>-0.156595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
@@ -1347,16 +1399,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000382</td>\n",
-       "      <td>-0.000466</td>\n",
-       "      <td>-0.000408</td>\n",
-       "      <td>-0.000091</td>\n",
-       "      <td>-0.000512</td>\n",
-       "      <td>-0.000240</td>\n",
-       "      <td>-0.000283</td>\n",
-       "      <td>-1.361221e-04</td>\n",
-       "      <td>-0.000274</td>\n",
-       "      <td>-0.000745</td>\n",
+       "      <td>0.100300</td>\n",
+       "      <td>0.018010</td>\n",
+       "      <td>0.212837</td>\n",
+       "      <td>0.573748</td>\n",
+       "      <td>0.151573</td>\n",
+       "      <td>0.110227</td>\n",
+       "      <td>0.088412</td>\n",
+       "      <td>0.286110</td>\n",
+       "      <td>0.165036</td>\n",
+       "      <td>0.329597</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1415,104 +1467,104 @@
        "47  chr1  109728807  TTT   G        ALX3  109710224  110234512      -   \n",
        "\n",
        "    gene_mask_start  gene_mask_end  ...  agg_9528  agg_9529  agg_9530  \\\n",
-       "0            163840         172672  ... -0.000053 -0.000153 -0.000089   \n",
-       "1            163840         178946  ... -0.000586 -0.000891 -0.000565   \n",
-       "2            163840         170729  ... -0.000565 -0.000787 -0.000515   \n",
-       "3            163840         165050  ... -0.001453 -0.001775 -0.001403   \n",
-       "4            163840         166306  ...  0.000045  0.000105  0.000060   \n",
-       "5            163840         167179  ... -0.000561 -0.000826 -0.000536   \n",
-       "6            163840         198383  ... -0.000399 -0.000585 -0.000374   \n",
-       "7            163840         184493  ...  0.001317  0.001001  0.000884   \n",
-       "8            163840         168975  ... -0.000244 -0.000214 -0.000310   \n",
-       "9            163840         173223  ...  0.000012 -0.000113  0.000002   \n",
-       "10           163840         170339  ... -0.000286 -0.000486 -0.000312   \n",
-       "11           163840         177242  ...  0.007558  0.004934  0.007966   \n",
-       "12           163840         166924  ... -0.000190 -0.000248 -0.000215   \n",
-       "13           163840         166653  ... -0.000217 -0.000332 -0.000225   \n",
-       "14           163840         199838  ... -0.000643 -0.000886 -0.000513   \n",
-       "15           163840         178999  ... -0.000031 -0.000034 -0.000048   \n",
-       "16           163840         168116  ... -0.000396 -0.000607 -0.000421   \n",
-       "17           163840         183816  ... -0.000809 -0.001129 -0.000884   \n",
-       "18           163840         181059  ... -0.000826 -0.001292 -0.000932   \n",
-       "19           163840         176946  ... -0.000033 -0.000067 -0.000013   \n",
-       "20           163840         177982  ... -0.000297 -0.000456 -0.000343   \n",
-       "21           163840         172928  ... -0.000118 -0.000153 -0.000130   \n",
-       "22           163840         180515  ...  0.000099  0.000077  0.000057   \n",
-       "23           163840         180515  ...  0.003284  0.005105  0.002662   \n",
-       "24           163840         179428  ...  0.002200  0.001744  0.001206   \n",
-       "25           163840         179428  ... -0.004052 -0.003465 -0.003092   \n",
-       "26           163840         173165  ...  0.000001  0.000010  0.000091   \n",
-       "27           163840         173165  ...  0.000536 -0.000179  0.001585   \n",
-       "28           163840         172720  ...  0.000584  0.000577  0.000674   \n",
-       "29           163840         172720  ...  0.003762  0.002849  0.001487   \n",
-       "30           163840         172374  ...  0.000155  0.000251  0.000114   \n",
-       "31           163840         172374  ...  0.000258  0.000810  0.000308   \n",
-       "32           163840         170946  ... -0.000651 -0.000626 -0.000446   \n",
-       "33           163840         170946  ...  0.014822  0.029157  0.015316   \n",
-       "34           163840         233549  ...  0.002052  0.002637  0.003581   \n",
-       "35           163840         233549  ... -0.002514 -0.002176  0.001273   \n",
-       "36           163840         179789  ...  0.000870  0.001771  0.002003   \n",
-       "37           163840         179789  ...  0.015270  0.004848  0.011452   \n",
-       "38           163840         182577  ... -0.000336  0.001399  0.000511   \n",
-       "39           163840         182577  ... -0.032543 -0.033684 -0.029430   \n",
-       "40           163840         205369  ... -0.001145 -0.001371  0.000107   \n",
-       "41           163840         205369  ...  0.006410  0.010421  0.008430   \n",
-       "42           163840         185065  ...  0.000849  0.001993  0.001033   \n",
-       "43           163840         185065  ...  0.009973  0.012482  0.003073   \n",
-       "44           163840         227488  ...  0.002434  0.001946  0.001824   \n",
-       "45           163840         227488  ... -0.005434  0.003420 -0.019319   \n",
-       "46           163840         174642  ...  0.000210  0.000521  0.000256   \n",
-       "47           163840         174642  ... -0.000382 -0.000466 -0.000408   \n",
+       "0            163840         172672  ... -0.038816 -0.132628  0.012945   \n",
+       "1            163840         178946  ...  0.007422 -0.040294  0.037184   \n",
+       "2            163840         170729  ... -0.039081 -0.132857  0.012576   \n",
+       "3            163840         165050  ...  0.007090 -0.040752  0.036865   \n",
+       "4            163840         166306  ...  0.384038  0.383892  0.209059   \n",
+       "5            163840         167179  ...  0.128468  0.201122  0.096130   \n",
+       "6            163840         198383  ...  0.382609  0.387782  0.206949   \n",
+       "7            163840         184493  ...  0.127029  0.199535  0.092907   \n",
+       "8            163840         168975  ... -0.065035 -0.009808 -0.089304   \n",
+       "9            163840         173223  ...  0.149715  0.036652  0.236477   \n",
+       "10           163840         170339  ... -0.064055 -0.008839 -0.088600   \n",
+       "11           163840         177242  ...  0.148965  0.036048  0.236015   \n",
+       "12           163840         166924  ...  0.025167  0.316047  0.052610   \n",
+       "13           163840         166653  ... -0.060978  0.136151  0.222250   \n",
+       "14           163840         199838  ...  0.024651  0.315260  0.050833   \n",
+       "15           163840         178999  ... -0.069684  0.130147  0.214494   \n",
+       "16           163840         168116  ... -0.068721 -0.131200 -0.095311   \n",
+       "17           163840         183816  ... -0.000722 -0.065477 -0.015448   \n",
+       "18           163840         181059  ... -0.068239 -0.130473 -0.095077   \n",
+       "19           163840         176946  ... -0.000799 -0.065457 -0.015547   \n",
+       "20           163840         177982  ...  0.144724  0.096923  0.053529   \n",
+       "21           163840         172928  ... -0.204588 -0.164875 -0.161451   \n",
+       "22           163840         180515  ...  0.145371  0.099419  0.055633   \n",
+       "23           163840         180515  ... -0.216764 -0.175109 -0.172126   \n",
+       "24           163840         179428  ...  0.125082  0.187445  0.094098   \n",
+       "25           163840         179428  ... -0.072443 -0.081550 -0.038486   \n",
+       "26           163840         173165  ...  0.125229  0.187538  0.094372   \n",
+       "27           163840         173165  ... -0.072734 -0.082032 -0.038806   \n",
+       "28           163840         172720  ... -0.109239 -0.095734 -0.046736   \n",
+       "29           163840         172720  ... -0.014338 -0.047306  0.040593   \n",
+       "30           163840         172374  ... -0.114962 -0.101682 -0.051288   \n",
+       "31           163840         172374  ... -0.014739 -0.051509  0.039989   \n",
+       "32           163840         170946  ...  0.017597 -0.080504  0.049995   \n",
+       "33           163840         170946  ... -0.297318  0.036640  0.039409   \n",
+       "34           163840         233549  ...  0.016951 -0.081113  0.048497   \n",
+       "35           163840         233549  ... -0.296372  0.026870  0.034203   \n",
+       "36           163840         179789  ...  0.172656  0.238587 -0.112662   \n",
+       "37           163840         179789  ... -0.587357 -0.383792 -0.075256   \n",
+       "38           163840         182577  ...  0.196620  0.244671 -0.095485   \n",
+       "39           163840         182577  ... -0.598168 -0.350979 -0.061809   \n",
+       "40           163840         205369  ... -0.065015 -0.134536 -0.140023   \n",
+       "41           163840         205369  ... -0.138112 -0.135524 -0.122887   \n",
+       "42           163840         185065  ... -0.064923 -0.134493 -0.140439   \n",
+       "43           163840         185065  ... -0.138915 -0.137064 -0.123798   \n",
+       "44           163840         227488  ... -0.260050 -0.168407 -0.271245   \n",
+       "45           163840         227488  ...  0.107971  0.020257  0.216171   \n",
+       "46           163840         174642  ... -0.256655 -0.163457 -0.266899   \n",
+       "47           163840         174642  ...  0.100300  0.018010  0.212837   \n",
        "\n",
-       "    agg_9531  agg_9532  agg_9533  agg_9535      agg_9536  agg_9537  agg_9538  \n",
-       "0  -0.000016 -0.000013 -0.000040 -0.000070  7.629395e-05 -0.000026 -0.000069  \n",
-       "1  -0.000311 -0.000461 -0.000252 -0.000376 -6.060898e-04 -0.000454 -0.000725  \n",
-       "2  -0.000279 -0.000354 -0.000202 -0.000342 -5.399883e-04 -0.000423 -0.000597  \n",
-       "3  -0.000820 -0.000896 -0.000575 -0.001113 -1.119316e-03 -0.001036 -0.001202  \n",
-       "4  -0.000030 -0.000049  0.000029  0.000135  1.490116e-07  0.000057  0.000001  \n",
-       "5  -0.000299 -0.000324 -0.000196 -0.000374 -3.589988e-04 -0.000342 -0.000426  \n",
-       "6  -0.000208 -0.000212 -0.000126 -0.000265 -2.926290e-04 -0.000266 -0.000376  \n",
-       "7   0.001013  0.000691  0.001310  0.001097  5.598068e-04  0.000501  0.000827  \n",
-       "8  -0.000153 -0.000324 -0.000168 -0.000147 -4.823208e-04 -0.000211 -0.000401  \n",
-       "9  -0.000028 -0.000248 -0.000283 -0.000314 -1.890063e-04 -0.000022 -0.000029  \n",
-       "10 -0.000150 -0.000256 -0.000124 -0.000179 -3.057718e-04 -0.000183 -0.000252  \n",
-       "11  0.007595 -0.001943  0.001598  0.007838  2.528191e-03  0.007137  0.009534  \n",
-       "12 -0.000113 -0.000070 -0.000076 -0.000159 -5.897880e-05 -0.000117 -0.000182  \n",
-       "13 -0.000132 -0.000127 -0.000102 -0.000146 -2.127588e-04 -0.000168 -0.000276  \n",
-       "14 -0.001688 -0.000346 -0.000528 -0.000894 -4.293919e-04 -0.001309 -0.000301  \n",
-       "15 -0.000017  0.000019 -0.000002 -0.000036 -1.719594e-05 -0.000026 -0.000037  \n",
-       "16 -0.000306 -0.000294 -0.000207 -0.000237 -3.867447e-04 -0.000289 -0.000498  \n",
-       "17 -0.000664 -0.000741 -0.000528 -0.000512 -7.908940e-04 -0.000607 -0.000990  \n",
-       "18 -0.000710 -0.000789 -0.000506 -0.000457 -9.316206e-04 -0.000666 -0.001122  \n",
-       "19 -0.000010  0.000030  0.000017 -0.000020  1.147389e-04 -0.000005 -0.000017  \n",
-       "20 -0.000253 -0.000310 -0.000204 -0.000172 -3.470182e-04 -0.000220 -0.000371  \n",
-       "21 -0.000090 -0.000128 -0.000076 -0.000053 -2.347827e-04 -0.000090 -0.000214  \n",
-       "22  0.000040  0.000018  0.000047  0.000075  2.750456e-04  0.000065  0.000258  \n",
-       "23  0.001423  0.001525  0.001257  0.001919  5.420089e-03  0.002166  0.005263  \n",
-       "24  0.001263  0.001008 -0.000117  0.001218  2.799034e-04  0.001503  0.001372  \n",
-       "25 -0.003744 -0.003384 -0.003265 -0.003253  1.600981e-03 -0.001915 -0.002728  \n",
-       "26 -0.000079 -0.000044 -0.000061 -0.000042 -4.082918e-05 -0.000022 -0.000060  \n",
-       "27  0.001431  0.003103  0.001631  0.000257  2.081454e-03  0.000913  0.001677  \n",
-       "28  0.000536  0.000167  0.000397  0.000507  1.705885e-04  0.000503  0.000481  \n",
-       "29  0.002733 -0.000099  0.000631  0.001469  4.820824e-04  0.002098 -0.000244  \n",
-       "30  0.000134 -0.000137 -0.000068  0.000125  4.252791e-05  0.000117  0.000137  \n",
-       "31  0.000489  0.000618  0.000236  0.000523  6.991625e-04  0.000155  0.000536  \n",
-       "32 -0.000290 -0.000174 -0.000026 -0.000154 -3.945976e-04 -0.000396 -0.000499  \n",
-       "33  0.007980  0.015211  0.007929  0.010251  2.181430e-02  0.009214  0.024373  \n",
-       "34  0.001084 -0.001867 -0.003804  0.003472 -1.005411e-03  0.003400  0.001397  \n",
-       "35  0.007376  0.011271  0.019305 -0.002886  2.035785e-02 -0.001660  0.006990  \n",
-       "36  0.000641 -0.002239 -0.000491  0.001610 -6.132126e-04  0.002603  0.000714  \n",
-       "37  0.028524  0.009958  0.013799  0.007597  2.641273e-02  0.009691  0.026180  \n",
-       "38  0.000450 -0.000501 -0.000243 -0.001328  4.479885e-04  0.000544  0.002848  \n",
-       "39 -0.050647 -0.030112 -0.021851 -0.012187 -2.968431e-02 -0.037312 -0.067244  \n",
-       "40 -0.000453 -0.001597 -0.000798 -0.001882  1.063347e-04  0.000106  0.000086  \n",
-       "41  0.010849 -0.004994  0.003090 -0.004843 -9.270430e-03  0.008755  0.011827  \n",
-       "42 -0.000050 -0.000534  0.000095  0.000409 -3.244877e-04  0.000842  0.000388  \n",
-       "43  0.019083  0.010606  0.000387  0.003512  7.757962e-03  0.011860  0.019928  \n",
-       "44  0.000374 -0.000416 -0.000871  0.003142 -1.690984e-03  0.001557  0.002596  \n",
-       "45 -0.000747  0.041849  0.022034 -0.024061  4.308212e-02  0.005673 -0.007195  \n",
-       "46  0.000118  0.000335  0.000174  0.000136  8.502305e-04  0.000165  0.000544  \n",
-       "47 -0.000091 -0.000512 -0.000240 -0.000283 -1.361221e-04 -0.000274 -0.000745  \n",
+       "    agg_9531  agg_9532  agg_9533  agg_9535  agg_9536  agg_9537  agg_9538  \n",
+       "0  -0.057034 -0.045095 -0.019845 -0.048861 -0.248287 -0.157361 -0.202191  \n",
+       "1  -0.039102 -0.069273 -0.089758  0.085668 -0.100051  0.199948 -0.228135  \n",
+       "2  -0.057286 -0.044211 -0.019335 -0.049245 -0.247860 -0.158028 -0.202599  \n",
+       "3  -0.038709 -0.069798 -0.090022  0.085745 -0.100093  0.199475 -0.228633  \n",
+       "4   0.197611  0.147189  0.214235  0.429188  0.046692  0.232973  0.122444  \n",
+       "5   0.117577 -0.126177 -0.059776  0.184826 -0.009697  0.305417 -0.107728  \n",
+       "6   0.192670  0.148521  0.211755  0.428720  0.039945  0.232015  0.117866  \n",
+       "7   0.114106 -0.132683 -0.060256  0.180594 -0.008448  0.303062 -0.112591  \n",
+       "8  -0.123990 -0.237687 -0.145043 -0.027265 -0.172405 -0.209000 -0.169588  \n",
+       "9  -0.033082 -0.059123 -0.109544 -0.116550 -0.056748  0.238428 -0.066090  \n",
+       "10 -0.123287 -0.236878 -0.144462 -0.026132 -0.171725 -0.208052 -0.169746  \n",
+       "11 -0.033107 -0.059445 -0.110096 -0.117363 -0.057286  0.237902 -0.067406  \n",
+       "12 -0.128209 -0.025701  0.018469  0.071030  0.035337  0.035106  0.198629  \n",
+       "13  0.180415 -0.077709  0.059230 -0.152584  0.021212  0.387905  0.087836  \n",
+       "14 -0.121852 -0.024862  0.020706  0.065433  0.039559  0.038879  0.207624  \n",
+       "15  0.163851 -0.088984  0.049194 -0.161672  0.012433  0.376862  0.079508  \n",
+       "16  0.046062  0.058227 -0.014569 -0.110806  0.156508  0.050760  0.086102  \n",
+       "17 -0.048512  0.080478  0.044108 -0.040190 -0.109651  0.016822 -0.062620  \n",
+       "18  0.046099  0.058977 -0.014226 -0.110129  0.156933  0.051034  0.086115  \n",
+       "19 -0.048479  0.080698  0.044615 -0.040375 -0.109146  0.016773 -0.063049  \n",
+       "20  0.178255  0.186835 -0.086366  0.078235  0.339634  0.164180  0.351917  \n",
+       "21 -0.077614  0.001604 -0.079316 -0.283131 -0.060867 -0.083952 -0.061946  \n",
+       "22  0.177352  0.190575 -0.084158  0.083270  0.344398  0.166967  0.355001  \n",
+       "23 -0.086336 -0.018204 -0.098414 -0.295143 -0.081368 -0.096516 -0.078732  \n",
+       "24  0.073541  0.153524  0.122184  0.178186  0.325020  0.131711  0.251710  \n",
+       "25 -0.047714  0.226176  0.045261 -0.126760  0.371777  0.060598 -0.005164  \n",
+       "26  0.073884  0.154733  0.122949  0.178414  0.326226  0.132067  0.251894  \n",
+       "27 -0.047959  0.226084  0.045132 -0.126927  0.371727  0.060446 -0.005538  \n",
+       "28 -0.160156 -0.108054 -0.116374  0.049736  0.170926  0.112000  0.250690  \n",
+       "29  0.034276  0.086477 -0.002776 -0.036525  0.105931  0.075943 -0.094462  \n",
+       "30 -0.167562 -0.114795 -0.118255  0.045381  0.164219  0.106147  0.242618  \n",
+       "31  0.034758  0.078923 -0.006194 -0.037574  0.102354  0.074973 -0.093299  \n",
+       "32 -0.011808 -0.271627 -0.061634 -0.068092 -0.169892 -0.139209 -0.134266  \n",
+       "33  0.172793  0.131960  0.099102 -0.127269  0.248293  0.078142  0.244484  \n",
+       "34 -0.012860 -0.272076 -0.061712 -0.068612 -0.168814 -0.140736 -0.135443  \n",
+       "35  0.171992  0.132727  0.100970 -0.133759  0.247039  0.073710  0.242920  \n",
+       "36 -0.397093 -0.048434 -0.349497  0.178258 -0.541721 -0.432872 -0.115762  \n",
+       "37  0.116061 -0.107381 -0.537354 -0.290173  0.274848 -0.387343 -0.475388  \n",
+       "38 -0.382966 -0.053531 -0.346830  0.205384 -0.547652 -0.427548 -0.109037  \n",
+       "39  0.137094 -0.114290 -0.549722 -0.316452  0.281865 -0.367328 -0.457750  \n",
+       "40  0.024319 -0.060507 -0.108153 -0.149791 -0.038775 -0.076944 -0.118178  \n",
+       "41  0.003460 -0.095921 -0.142851 -0.133408 -0.044298 -0.079864  0.063350  \n",
+       "42  0.024307 -0.060060 -0.106959 -0.150438 -0.038277 -0.077278 -0.118272  \n",
+       "43  0.002397 -0.096466 -0.143198 -0.133723 -0.044947 -0.080987  0.062265  \n",
+       "44 -0.247335 -0.234919 -0.383042 -0.163728 -0.055309 -0.088513 -0.155641  \n",
+       "45  0.572421  0.147785  0.106555  0.096098  0.279161  0.162524  0.332554  \n",
+       "46 -0.247330 -0.232560 -0.386224 -0.155104 -0.059262 -0.081452 -0.156595  \n",
+       "47  0.573748  0.151573  0.110227  0.088412  0.286110  0.165036  0.329597  \n",
        "\n",
        "[48 rows x 8870 columns]"
       ]
@@ -1523,20 +1575,388 @@
     }
    ],
    "source": [
-    "pd.read_parquet(\"vep_vcf_results.parquet\")"
+    "results = pd.read_parquet(\"vep_vcf_results.parquet\")\n",
+    "results"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively, you can pass tsv file with following format where first 4 columns are `chrom`, `pos`, `ref`, `alt`."
+    "Now, we need to match the track labels (agg_1, agg_2, etc.) to their metadata. You can load the metadata as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>organ</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>study</th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>region</th>\n",
+       "      <th>subregion</th>\n",
+       "      <th>celltype_coarse</th>\n",
+       "      <th>n_cells</th>\n",
+       "      <th>total_counts</th>\n",
+       "      <th>n_genes</th>\n",
+       "      <th>size_factor</th>\n",
+       "      <th>train_pearson</th>\n",
+       "      <th>val_pearson</th>\n",
+       "      <th>test_pearson</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>agg_0</th>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Amygdala</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>jhpce#tran2021</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>331</td>\n",
+       "      <td>1.592883e+07</td>\n",
+       "      <td>17000</td>\n",
+       "      <td>41431.465186</td>\n",
+       "      <td>0.942459</td>\n",
+       "      <td>0.841377</td>\n",
+       "      <td>0.865640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_1</th>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Basolateral nuclear group (BLN) - lat...</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Basolateral nuclear group (BLN) - lateral nucl...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11369</td>\n",
+       "      <td>2.952133e+08</td>\n",
+       "      <td>18080</td>\n",
+       "      <td>40765.341481</td>\n",
+       "      <td>0.943098</td>\n",
+       "      <td>0.838936</td>\n",
+       "      <td>0.861092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_2</th>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Bed nucleus of stria terminalis and n...</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Bed nucleus of stria terminalis and nearby - BNST</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>139</td>\n",
+       "      <td>2.593231e+06</td>\n",
+       "      <td>15418</td>\n",
+       "      <td>42556.387020</td>\n",
+       "      <td>0.952170</td>\n",
+       "      <td>0.854544</td>\n",
+       "      <td>0.866654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_3</th>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Central nuclear group - CEN</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Central nuclear group - CEN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3892</td>\n",
+       "      <td>9.946371e+07</td>\n",
+       "      <td>17959</td>\n",
+       "      <td>42884.641430</td>\n",
+       "      <td>0.959744</td>\n",
+       "      <td>0.863585</td>\n",
+       "      <td>0.881554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_4</th>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Corticomedial nuclear group (CMN) - a...</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Corticomedial nuclear group (CMN) - anterior c...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2945</td>\n",
+       "      <td>1.281619e+08</td>\n",
+       "      <td>17885</td>\n",
+       "      <td>41816.741933</td>\n",
+       "      <td>0.951365</td>\n",
+       "      <td>0.854304</td>\n",
+       "      <td>0.868902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9533</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>upper lobe of right lung</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3.483375e+04</td>\n",
+       "      <td>8515</td>\n",
+       "      <td>35404.911768</td>\n",
+       "      <td>0.735213</td>\n",
+       "      <td>0.665647</td>\n",
+       "      <td>0.654491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9535</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>urinary bladder</td>\n",
+       "      <td>urinary</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>GSE129845</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8.498500e+04</td>\n",
+       "      <td>7337</td>\n",
+       "      <td>26189.415789</td>\n",
+       "      <td>0.809852</td>\n",
+       "      <td>0.690022</td>\n",
+       "      <td>0.656160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9536</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>272</td>\n",
+       "      <td>5.700762e+05</td>\n",
+       "      <td>14769</td>\n",
+       "      <td>44938.403867</td>\n",
+       "      <td>0.915329</td>\n",
+       "      <td>0.808941</td>\n",
+       "      <td>0.839993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9537</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>472</td>\n",
+       "      <td>1.089170e+07</td>\n",
+       "      <td>14514</td>\n",
+       "      <td>30145.422152</td>\n",
+       "      <td>0.852339</td>\n",
+       "      <td>0.717682</td>\n",
+       "      <td>0.727469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9538</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>vasculature</td>\n",
+       "      <td>vasculature</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>5.992697e+07</td>\n",
+       "      <td>16764</td>\n",
+       "      <td>36464.273371</td>\n",
+       "      <td>0.909855</td>\n",
+       "      <td>0.780413</td>\n",
+       "      <td>0.796351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8856 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       cell_type  \\\n",
+       "agg_0                        Amygdala excitatory   \n",
+       "agg_1                        Amygdala excitatory   \n",
+       "agg_2                        Amygdala excitatory   \n",
+       "agg_3                        Amygdala excitatory   \n",
+       "agg_4                        Amygdala excitatory   \n",
+       "...                                          ...   \n",
+       "agg_9533  vascular associated smooth muscle cell   \n",
+       "agg_9535  vascular associated smooth muscle cell   \n",
+       "agg_9536  vascular associated smooth muscle cell   \n",
+       "agg_9537  vascular associated smooth muscle cell   \n",
+       "agg_9538  vascular associated smooth muscle cell   \n",
+       "\n",
+       "                                                     tissue        organ  \\\n",
+       "agg_0                                     Amygdala_Amygdala          CNS   \n",
+       "agg_1     Amygdala_Basolateral nuclear group (BLN) - lat...          CNS   \n",
+       "agg_2     Amygdala_Bed nucleus of stria terminalis and n...          CNS   \n",
+       "agg_3                  Amygdala_Central nuclear group - CEN          CNS   \n",
+       "agg_4     Amygdala_Corticomedial nuclear group (CMN) - a...          CNS   \n",
+       "...                                                     ...          ...   \n",
+       "agg_9533                           upper lobe of right lung         lung   \n",
+       "agg_9535                                    urinary bladder      urinary   \n",
+       "agg_9536                                             uterus       uterus   \n",
+       "agg_9537                                             uterus       uterus   \n",
+       "agg_9538                                        vasculature  vasculature   \n",
+       "\n",
+       "          disease                                 study      dataset  \\\n",
+       "agg_0     healthy                        jhpce#tran2021  brain_atlas   \n",
+       "agg_1     healthy                            SCR_016152  brain_atlas   \n",
+       "agg_2     healthy                            SCR_016152  brain_atlas   \n",
+       "agg_3     healthy                            SCR_016152  brain_atlas   \n",
+       "agg_4     healthy                            SCR_016152  brain_atlas   \n",
+       "...           ...                                   ...          ...   \n",
+       "agg_9533       NA                                ENCODE  scimilarity   \n",
+       "agg_9535  healthy                             GSE129845  scimilarity   \n",
+       "agg_9536       NA                                ENCODE  scimilarity   \n",
+       "agg_9537  healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity   \n",
+       "agg_9538  healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity   \n",
+       "\n",
+       "            region                                          subregion  \\\n",
+       "agg_0     Amygdala                                           Amygdala   \n",
+       "agg_1     Amygdala  Basolateral nuclear group (BLN) - lateral nucl...   \n",
+       "agg_2     Amygdala  Bed nucleus of stria terminalis and nearby - BNST   \n",
+       "agg_3     Amygdala                        Central nuclear group - CEN   \n",
+       "agg_4     Amygdala  Corticomedial nuclear group (CMN) - anterior c...   \n",
+       "...            ...                                                ...   \n",
+       "agg_9533       nan                                                nan   \n",
+       "agg_9535       nan                                                nan   \n",
+       "agg_9536       nan                                                nan   \n",
+       "agg_9537       nan                                                nan   \n",
+       "agg_9538       nan                                                nan   \n",
+       "\n",
+       "         celltype_coarse  n_cells  total_counts  n_genes   size_factor  \\\n",
+       "agg_0                NaN      331  1.592883e+07    17000  41431.465186   \n",
+       "agg_1                NaN    11369  2.952133e+08    18080  40765.341481   \n",
+       "agg_2                NaN      139  2.593231e+06    15418  42556.387020   \n",
+       "agg_3                NaN     3892  9.946371e+07    17959  42884.641430   \n",
+       "agg_4                NaN     2945  1.281619e+08    17885  41816.741933   \n",
+       "...                  ...      ...           ...      ...           ...   \n",
+       "agg_9533             NaN       21  3.483375e+04     8515  35404.911768   \n",
+       "agg_9535             NaN       24  8.498500e+04     7337  26189.415789   \n",
+       "agg_9536             NaN      272  5.700762e+05    14769  44938.403867   \n",
+       "agg_9537             NaN      472  1.089170e+07    14514  30145.422152   \n",
+       "agg_9538             NaN     1853  5.992697e+07    16764  36464.273371   \n",
+       "\n",
+       "          train_pearson  val_pearson  test_pearson  \n",
+       "agg_0          0.942459     0.841377      0.865640  \n",
+       "agg_1          0.943098     0.838936      0.861092  \n",
+       "agg_2          0.952170     0.854544      0.866654  \n",
+       "agg_3          0.959744     0.863585      0.881554  \n",
+       "agg_4          0.951365     0.854304      0.868902  \n",
+       "...                 ...          ...           ...  \n",
+       "agg_9533       0.735213     0.665647      0.654491  \n",
+       "agg_9535       0.809852     0.690022      0.656160  \n",
+       "agg_9536       0.915329     0.808941      0.839993  \n",
+       "agg_9537       0.852339     0.717682      0.727469  \n",
+       "agg_9538       0.909855     0.780413      0.796351  \n",
+       "\n",
+       "[8856 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from decima import DecimaResult\n",
+    "metadata = DecimaResult.load().cell_metadata\n",
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of a vcf, you can also pass a tsv file with the following format where the first 4 columns are `chrom`, `pos`, `ref`, `alt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1559,83 +1979,65 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can only run predictions for the variants closer to tss than 100kbp anyway these are the ones likely to be most impactful on the gene expression."
+    "You can limit predictions to variant-gene pairs with a maximum distance (say 100kbp)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "decima.vep - INFO - Using device: cuda and genome: hg38\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcelikm5\u001b[0m (\u001b[33mcelikm5-genentech\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1276.1MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.1 (1916.9MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1417.4MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "decima - INFO - Performing predictions on VariantDataset(33 variants from ['chr1'] between 598861 and 110072228 bp from TSS)\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima vep -v data/varian ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=95` in the `DataLoader` to improve performance.\n",
-      "Predicting DataLoader 0: 100%|██████████████████| 66/66 [00:13<00:00,  4.88it/s]\n",
-      "decima.vep - INFO - Warnings:\n",
-      "decima.vep - INFO - allele_mismatch_with_reference_genome: 10 alleles out of 33 predictions mismatched with the genome file /home/celikm5/.local/share/genomes/hg38/hg38.fa.If this is not expected, please check if you are using the correct genome version.\n",
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 66/66 \u001b[2m0:00:31 • 0:00:00\u001b[0m \u001b[2;4m2.09it/s\u001b[0m it/s\u001b[0m it/s\u001b[0m \n",
+      "\u001b[?25hdecima - WARNING - Warnings:\n",
+      "decima - WARNING - allele_mismatch_with_reference_genome: 5 alleles out of 33 predictions mismatched with the genome file /home/lala8/.local/share/genomes/hg38/hg38.fa. If this is not expected, please check if you are using the correct genome version.\n",
       "\u001b[0m"
      ]
     }
    ],
    "source": [
-    "! decima vep -v \"data/variants.tsv\" -o \"vep_results.parquet\" --max-distance 100_000 --max-distance-type \"tss\""
+    "! decima vep -v \"data/variants.tsv\" -o \"vep_results.parquet\" --max-distance 100_000 --distance-type \"tss\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you have already have mapping genes and variant, you can use this mapping so predictions only will be conducted between this pairs."
+    "If you have already have a mapping between genes and variants, you can use this mapping so predictions will only will be computed between these pairs. Otherwise, the variant effect will be computed for all genes within Decima's distance window."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "decima.vep - INFO - Using device: cuda and genome: hg38\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcelikm5\u001b[0m (\u001b[33mcelikm5-genentech\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1311.1MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.1 (1900.4MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1475.9MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "decima - INFO - Performing predictions on VariantDataset(2 variants from ['chr1'] between 837298 and 1361586 bp from TSS)\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima vep -v data/varian ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=95` in the `DataLoader` to improve performance.\n",
-      "Predicting DataLoader 0: 100%|████████████████████| 4/4 [00:01<00:00,  3.79it/s]\n",
-      "\u001b[0m"
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 4/4 \u001b[2m0:00:02 • 0:00:00\u001b[0m \u001b[2;4m1.65it/s\u001b[0m [2;4m1.97it/s\u001b[0m \n",
+      "\u001b[?25h\u001b[0m"
      ]
     }
    ],
@@ -1645,7 +2047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -1706,16 +2108,16 @@
        "      <td>163840</td>\n",
        "      <td>177242</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000746</td>\n",
-       "      <td>0.002301</td>\n",
-       "      <td>0.005067</td>\n",
-       "      <td>0.000135</td>\n",
-       "      <td>-0.000559</td>\n",
-       "      <td>-0.003155</td>\n",
-       "      <td>0.006503</td>\n",
-       "      <td>-0.001059</td>\n",
-       "      <td>-0.000566</td>\n",
-       "      <td>0.000964</td>\n",
+       "      <td>-0.757413</td>\n",
+       "      <td>-0.096282</td>\n",
+       "      <td>-0.507625</td>\n",
+       "      <td>-1.093171</td>\n",
+       "      <td>-0.618375</td>\n",
+       "      <td>-1.054209</td>\n",
+       "      <td>-0.035263</td>\n",
+       "      <td>-0.046396</td>\n",
+       "      <td>-0.040251</td>\n",
+       "      <td>-0.128908</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1730,16 +2132,16 @@
        "      <td>163840</td>\n",
        "      <td>177242</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.007558</td>\n",
-       "      <td>0.004934</td>\n",
-       "      <td>0.007966</td>\n",
-       "      <td>0.007595</td>\n",
-       "      <td>-0.001943</td>\n",
-       "      <td>0.001598</td>\n",
-       "      <td>0.007838</td>\n",
-       "      <td>0.002528</td>\n",
-       "      <td>0.007137</td>\n",
-       "      <td>0.009534</td>\n",
+       "      <td>0.960004</td>\n",
+       "      <td>0.592360</td>\n",
+       "      <td>1.353235</td>\n",
+       "      <td>2.281869</td>\n",
+       "      <td>0.917179</td>\n",
+       "      <td>0.980594</td>\n",
+       "      <td>0.889794</td>\n",
+       "      <td>1.287064</td>\n",
+       "      <td>0.981355</td>\n",
+       "      <td>1.071923</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1752,17 +2154,17 @@
        "1  chr1  1002308   T   C  ISG15  837298  1361586      +           163840   \n",
        "\n",
        "   gene_mask_end  ...  agg_9528  agg_9529  agg_9530  agg_9531  agg_9532  \\\n",
-       "0         177242  ... -0.000746  0.002301  0.005067  0.000135 -0.000559   \n",
-       "1         177242  ...  0.007558  0.004934  0.007966  0.007595 -0.001943   \n",
+       "0         177242  ... -0.757413 -0.096282 -0.507625 -1.093171 -0.618375   \n",
+       "1         177242  ...  0.960004  0.592360  1.353235  2.281869  0.917179   \n",
        "\n",
        "   agg_9533  agg_9535  agg_9536  agg_9537  agg_9538  \n",
-       "0 -0.003155  0.006503 -0.001059 -0.000566  0.000964  \n",
-       "1  0.001598  0.007838  0.002528  0.007137  0.009534  \n",
+       "0 -1.054209 -0.035263 -0.046396 -0.040251 -0.128908  \n",
+       "1  0.980594  0.889794  1.287064  0.981355  1.071923  \n",
        "\n",
        "[2 rows x 8870 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1775,77 +2177,46 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The vep api reads n (default=10_000) number of variants from vcf file performs predictions on these variants, saves them to parquet file then performs predictios for next next chuck. You can change chucksize:"
+    "The vep api reads n (default=10_000) number of variants from vcf file, performs predictions on these variants, saves them to a parquet file,  then performs predictions for the next chunk. You can change the chunksize:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "decima.vep - INFO - Using device: cuda and genome: hg38\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcelikm5\u001b[0m (\u001b[33mcelikm5-genentech\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1365.3MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.2 (1851.4MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1323.7MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "decima - INFO - Performing predictions on VariantDataset(22 variants from ['chr1'] between 516455 and 1525617 bp from TSS)\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima vep -v data/sample ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=95` in the `DataLoader` to improve performance.\n",
-      "Predicting DataLoader 0: 100%|██████████████████| 44/44 [00:09<00:00,  4.86it/s]\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1340.8MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.1 (1944.0MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.4 (1576.5MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 44/44 \u001b[2m0:00:18 • 0:00:00\u001b[0m \u001b[2;4m2.31it/s\u001b[0m it/s\u001b[0m it/s\u001b[0m \n",
+      "\u001b[?25hdecima - INFO - Performing predictions on VariantDataset(13 variants from ['chr1'] between 109259481 and 110234512 bp from TSS)\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima vep -v data/sample ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=95` in the `DataLoader` to improve performance.\n",
-      "Predicting DataLoader 0: 100%|██████████████████| 26/26 [00:05<00:00,  4.98it/s]\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1359.5MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.1 (1939.7MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1425.5MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 26/26 \u001b[2m0:00:10 • 0:00:00\u001b[0m \u001b[2;4m2.31it/s\u001b[0m [2;4m2.40it/s\u001b[0m \n",
+      "\u001b[?25hdecima - INFO - Performing predictions on VariantDataset(13 variants from ['chr1'] between 109259481 and 110234512 bp from TSS)\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima vep -v data/sample ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=95` in the `DataLoader` to improve performance.\n",
-      "Predicting DataLoader 0: 100%|██████████████████| 26/26 [00:05<00:00,  4.97it/s]\n",
-      "decima.vep - INFO - Warnings:\n",
-      "decima.vep - INFO - allele_mismatch_with_reference_genome: 26 alleles out of 48 predictions mismatched with the genome file /home/celikm5/.local/share/genomes/hg38/hg38.fa.If this is not expected, please check if you are using the correct genome version.\n",
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 26/26 \u001b[2m0:00:18 • 0:00:00\u001b[0m \u001b[2;4m1.41it/s\u001b[0m [2;4m1.35it/s\u001b[0m \n",
+      "\u001b[?25hdecima - WARNING - Warnings:\n",
+      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file /home/lala8/.local/share/genomes/hg38/hg38.fa. If this is not expected, please check if you are using the correct genome version.\n",
       "\u001b[0m"
      ]
     }
@@ -1870,7 +2241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1885,7 +2256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1964,7 +2335,7 @@
        "4  chr1  109728807    T  GG"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1978,50 +2349,123 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Simply pass your dataframe to `predict_variat_effect` function which will return dataframe for the prediction. You can pass `tasks` query to subset predictions for specific cells. Moreover, by default decima model for replicate 0 is used to use other replicates pass model=`1` , `2` or `3` to use other replicates or pass your custom model. If you pass `include_cols` argument the columns in the input will maintained in the output. To further variants based on distance to tss use `max_dist_tss` argument."
+    "Simply pass your dataframe to `predict_variant_effect` function which will return dataframe for the prediction. You can pass `tasks` query to subset predictions for specific cells. Moreover, by default the ensemble of 4 replicates is used. To use a specific replicate, pass model= `0`, `1` , `2` or `3` or pass your custom model. If you pass `include_cols` argument the columns in the input will be maintained in the output. To further select variants based on distance to tss use the `max_distance` argument."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[31mSignature:\u001b[39m\n",
+       "predict_variant_effect(\n",
+       "    df_variant: Union[pandas.core.frame.DataFrame, str],\n",
+       "    output_pq: Optional[str] = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    tasks: Union[str, List[str], NoneType] = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    model: Union[int, str, List[str]] = \u001b[33m'ensemble'\u001b[39m,\n",
+       "    metadata_anndata: Optional[str] = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    chunksize: int = \u001b[32m10000\u001b[39m,\n",
+       "    batch_size: int = \u001b[32m1\u001b[39m,\n",
+       "    num_workers: int = \u001b[32m16\u001b[39m,\n",
+       "    device: Optional[str] = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    include_cols: Optional[List[str]] = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    gene_col: Optional[str] = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    distance_type: Optional[str] = \u001b[33m'tss'\u001b[39m,\n",
+       "    min_distance: Optional[float] = \u001b[32m0\u001b[39m,\n",
+       "    max_distance: Optional[float] = inf,\n",
+       "    genome: str = \u001b[33m'hg38'\u001b[39m,\n",
+       "    save_replicates: bool = \u001b[38;5;28;01mFalse\u001b[39;00m,\n",
+       "    reference_cache: bool = \u001b[38;5;28;01mTrue\u001b[39;00m,\n",
+       "    float_precision: str = \u001b[33m'32'\u001b[39m,\n",
+       ") -> \u001b[38;5;28;01mNone\u001b[39;00m\n",
+       "\u001b[31mDocstring:\u001b[39m\n",
+       "Predict variant effect and save to parquet\n",
+       "\n",
+       "Args:\n",
+       "    df_variant (pd.DataFrame or str): DataFrame with variant information or path to variant file\n",
+       "    output_pq (str, optional): Path to save the parquet file. Defaults to None.\n",
+       "    tasks (str, optional): Tasks to predict. Defaults to None.\n",
+       "    model (int, optional): Model to use. Defaults to DEFAULT_ENSEMBLE.\n",
+       "    metadata_anndata (str, optional): Path to anndata file. Defaults to None.\n",
+       "    chunksize (int, optional): Number of variants to predict in each chunk. Defaults to 10_000.\n",
+       "    batch_size (int, optional): Batch size. Defaults to 1.\n",
+       "    num_workers (int, optional): Number of workers. Defaults to 16.\n",
+       "    device (str, optional): Device to use. Defaults to None.\n",
+       "    include_cols (list, optional): Columns to include in the output. Defaults to None.\n",
+       "    gene_col (str, optional): Column name for gene names. Defaults to None.\n",
+       "    distance_type (str, optional): Type of distance. Defaults to \"tss\".\n",
+       "    min_distance (float, optional): Minimum distance from the end of the gene. Defaults to 0 (inclusive).\n",
+       "    max_distance (float, optional): Maximum distance from the TSS. Defaults to inf (exclusive).\n",
+       "    genome (str, optional): Genome name or path to the genome fasta file. Defaults to \"hg38\".\n",
+       "    save_replicates (bool, optional): Save the replicates in the output. Defaults to False.\n",
+       "    reference_cache (bool, optional): Whether to use reference cache. Defaults to True.\n",
+       "    float_precision (str, optional): Floating-point precision. Defaults to \"32\".\n",
+       "\u001b[31mFile:\u001b[39m      ~/decima/src/decima/vep/vep.py\n",
+       "\u001b[31mType:\u001b[39m      function"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "predict_variant_effect?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcelikm5\u001b[0m (\u001b[33mcelikm5-genentech\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1339.8MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.2 (1791.0MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1495.3MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a73a894cc53a4b2f98c395e3b8c6bf72",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/rich/live.py:260: UserWarning: \n",
+       "install \"ipywidgets\" for Jupyter support\n",
+       "</pre>\n"
+      ],
       "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+       "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/rich/live.py:260: UserWarning: \n",
+       "install \"ipywidgets\" for Jupyter support\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warnings:\n",
+      "allele_mismatch_with_reference_genome: 13 alleles out of 82 predictions mismatched with the genome file /home/lala8/.local/share/genomes/hg38/hg38.fa. If this is not expected, please check if you are using the correct genome version.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
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+       "      <td>-0.093385</td>\n",
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@@ -2224,16 +2668,16 @@
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-       "      <td>0.005934</td>\n",
-       "      <td>-0.006544</td>\n",
+       "      <td>-0.390896</td>\n",
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+       "      <td>0.151064</td>\n",
+       "      <td>-0.122629</td>\n",
+       "      <td>0.059758</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>78</th>\n",
@@ -2248,16 +2692,16 @@
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
        "      <td>...</td>\n",
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-       "      <td>-0.144098</td>\n",
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-       "      <td>-0.091391</td>\n",
-       "      <td>-0.104009</td>\n",
-       "      <td>-0.034113</td>\n",
-       "      <td>-0.096020</td>\n",
-       "      <td>-0.063028</td>\n",
-       "      <td>-0.079460</td>\n",
-       "      <td>-0.102111</td>\n",
+       "      <td>-0.026459</td>\n",
+       "      <td>-0.118378</td>\n",
+       "      <td>0.084991</td>\n",
+       "      <td>-0.022175</td>\n",
+       "      <td>-0.209899</td>\n",
+       "      <td>-0.091537</td>\n",
+       "      <td>0.038770</td>\n",
+       "      <td>-0.119916</td>\n",
+       "      <td>-0.181460</td>\n",
+       "      <td>-0.167976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
@@ -2272,16 +2716,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000218</td>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.000263</td>\n",
-       "      <td>0.000122</td>\n",
-       "      <td>0.000352</td>\n",
-       "      <td>0.000184</td>\n",
-       "      <td>0.000139</td>\n",
-       "      <td>0.000887</td>\n",
-       "      <td>0.000173</td>\n",
-       "      <td>0.000558</td>\n",
+       "      <td>-0.413859</td>\n",
+       "      <td>-0.029041</td>\n",
+       "      <td>-0.079324</td>\n",
+       "      <td>0.070361</td>\n",
+       "      <td>0.022809</td>\n",
+       "      <td>-0.047733</td>\n",
+       "      <td>-0.206144</td>\n",
+       "      <td>0.133108</td>\n",
+       "      <td>-0.046837</td>\n",
+       "      <td>-0.011002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80</th>\n",
@@ -2296,16 +2740,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000218</td>\n",
-       "      <td>-0.000109</td>\n",
-       "      <td>-0.000204</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>-0.000265</td>\n",
-       "      <td>-0.000104</td>\n",
-       "      <td>-0.000189</td>\n",
-       "      <td>0.000523</td>\n",
-       "      <td>-0.000149</td>\n",
-       "      <td>-0.000385</td>\n",
+       "      <td>-0.085005</td>\n",
+       "      <td>-0.115757</td>\n",
+       "      <td>-0.085665</td>\n",
+       "      <td>-0.038702</td>\n",
+       "      <td>0.006083</td>\n",
+       "      <td>-0.039524</td>\n",
+       "      <td>-0.157262</td>\n",
+       "      <td>-0.003096</td>\n",
+       "      <td>-0.075535</td>\n",
+       "      <td>-0.172014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>81</th>\n",
@@ -2320,16 +2764,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.001127</td>\n",
-       "      <td>-0.002115</td>\n",
-       "      <td>-0.001278</td>\n",
-       "      <td>-0.000581</td>\n",
-       "      <td>-0.001344</td>\n",
-       "      <td>-0.000724</td>\n",
-       "      <td>-0.000569</td>\n",
-       "      <td>-0.003553</td>\n",
-       "      <td>-0.000857</td>\n",
-       "      <td>-0.002281</td>\n",
+       "      <td>-0.223234</td>\n",
+       "      <td>-0.099899</td>\n",
+       "      <td>-0.119575</td>\n",
+       "      <td>0.070561</td>\n",
+       "      <td>-0.016501</td>\n",
+       "      <td>-0.110469</td>\n",
+       "      <td>-0.208272</td>\n",
+       "      <td>0.088579</td>\n",
+       "      <td>-0.018304</td>\n",
+       "      <td>0.014062</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2351,35 +2795,35 @@
        "81  chr1  109728807    T  GG    ALX3  109710224  110234512      -   \n",
        "\n",
        "    gene_mask_start  gene_mask_end  ...  agg_9528  agg_9529  agg_9530  \\\n",
-       "0            163840         172672  ... -0.003487 -0.006149 -0.003175   \n",
-       "1            163840         172672  ... -0.000096 -0.000190 -0.000135   \n",
-       "2            163840         178946  ... -0.002595 -0.004256 -0.002759   \n",
-       "3            163840         178946  ... -0.000587 -0.000894 -0.000563   \n",
-       "4            163840         170729  ... -0.002933 -0.004738 -0.003541   \n",
+       "0            163840         172672  ... -0.006633 -0.043378 -0.031300   \n",
+       "1            163840         172672  ... -0.245506 -0.190218 -0.137243   \n",
+       "2            163840         178946  ... -0.005553 -0.041326 -0.028948   \n",
+       "3            163840         178946  ... -0.241619 -0.187318 -0.135041   \n",
+       "4            163840         170729  ...  0.152952  0.291293 -0.198963   \n",
        "..              ...            ...  ...       ...       ...       ...   \n",
-       "77           163840         227488  ... -0.004391  0.004506 -0.017141   \n",
-       "78           163840         227488  ... -0.129386 -0.144098 -0.077484   \n",
-       "79           163840         174642  ...  0.000218  0.000532  0.000263   \n",
-       "80           163840         174642  ... -0.000218 -0.000109 -0.000204   \n",
-       "81           163840         174642  ... -0.001127 -0.002115 -0.001278   \n",
+       "77           163840         227488  ... -0.390896 -0.056248 -0.131846   \n",
+       "78           163840         227488  ... -0.026459 -0.118378  0.084991   \n",
+       "79           163840         174642  ... -0.413859 -0.029041 -0.079324   \n",
+       "80           163840         174642  ... -0.085005 -0.115757 -0.085665   \n",
+       "81           163840         174642  ... -0.223234 -0.099899 -0.119575   \n",
        "\n",
        "    agg_9531  agg_9532  agg_9533  agg_9535  agg_9536  agg_9537  agg_9538  \n",
-       "0  -0.002726 -0.003147 -0.001746 -0.002291 -0.005283 -0.003393 -0.006694  \n",
-       "1  -0.000043 -0.000076 -0.000072 -0.000106  0.000029 -0.000055 -0.000119  \n",
-       "2  -0.001601 -0.002601 -0.001238 -0.001756 -0.002918 -0.001914 -0.003244  \n",
-       "3  -0.000324 -0.000471 -0.000258 -0.000382 -0.000683 -0.000459 -0.000757  \n",
-       "4  -0.001943 -0.002958 -0.001370 -0.002272 -0.003326 -0.002195 -0.003648  \n",
+       "0  -0.002222  0.046180 -0.025395 -0.086669  0.013435 -0.037723 -0.062421  \n",
+       "1   0.051545  0.003048 -0.128578 -0.236718  0.017593 -0.094332 -0.070269  \n",
+       "2   0.001374  0.048067 -0.024705 -0.086391  0.017870 -0.035195 -0.059956  \n",
+       "3   0.050612  0.005031 -0.125195 -0.233266  0.019467 -0.093385 -0.068222  \n",
+       "4  -0.430064  0.270837 -0.239649  0.068286 -0.406054 -0.293787 -0.101794  \n",
        "..       ...       ...       ...       ...       ...       ...       ...  \n",
-       "77  0.000025  0.042810  0.024843 -0.022073  0.043973  0.005934 -0.006544  \n",
-       "78 -0.091391 -0.104009 -0.034113 -0.096020 -0.063028 -0.079460 -0.102111  \n",
-       "79  0.000122  0.000352  0.000184  0.000139  0.000887  0.000173  0.000558  \n",
-       "80  0.000009 -0.000265 -0.000104 -0.000189  0.000523 -0.000149 -0.000385  \n",
-       "81 -0.000581 -0.001344 -0.000724 -0.000569 -0.003553 -0.000857 -0.002281  \n",
+       "77  0.002266  0.052115 -0.073442 -0.171080  0.151064 -0.122629  0.059758  \n",
+       "78 -0.022175 -0.209899 -0.091537  0.038770 -0.119916 -0.181460 -0.167976  \n",
+       "79  0.070361  0.022809 -0.047733 -0.206144  0.133108 -0.046837 -0.011002  \n",
+       "80 -0.038702  0.006083 -0.039524 -0.157262 -0.003096 -0.075535 -0.172014  \n",
+       "81  0.070561 -0.016501 -0.110469 -0.208272  0.088579 -0.018304  0.014062  \n",
        "\n",
        "[82 rows x 8870 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2397,42 +2841,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1289.8MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.2 (1774.2MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1430.2MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f9c815b42b1b4ba6ad1edb5427a53db6",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/rich/live.py:260: UserWarning: \n",
+       "install \"ipywidgets\" for Jupyter support\n",
+       "</pre>\n"
+      ],
       "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+       "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/rich/live.py:260: UserWarning: \n",
+       "install \"ipywidgets\" for Jupyter support\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warnings:\n",
+      "allele_mismatch_with_reference_genome: 13 alleles out of 82 predictions mismatched with the genome file /home/lala8/.local/share/genomes/hg38/hg38.fa. If this is not expected, please check if you are using the correct genome version.\n"
+     ]
     }
    ],
    "source": [
@@ -2441,7 +2896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2502,16 +2957,16 @@
        "      <td>163840</td>\n",
        "      <td>172672</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.003487</td>\n",
-       "      <td>-0.006149</td>\n",
-       "      <td>-0.003175</td>\n",
-       "      <td>-0.002726</td>\n",
-       "      <td>-0.003147</td>\n",
-       "      <td>-0.001746</td>\n",
-       "      <td>-0.002291</td>\n",
-       "      <td>-0.005283</td>\n",
-       "      <td>-0.003393</td>\n",
-       "      <td>-0.006694</td>\n",
+       "      <td>-0.006633</td>\n",
+       "      <td>-0.043378</td>\n",
+       "      <td>-0.031300</td>\n",
+       "      <td>-0.002222</td>\n",
+       "      <td>0.046180</td>\n",
+       "      <td>-0.025395</td>\n",
+       "      <td>-0.086669</td>\n",
+       "      <td>0.013435</td>\n",
+       "      <td>-0.037723</td>\n",
+       "      <td>-0.062421</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2526,16 +2981,16 @@
        "      <td>163840</td>\n",
        "      <td>172672</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000096</td>\n",
-       "      <td>-0.000190</td>\n",
-       "      <td>-0.000135</td>\n",
-       "      <td>-0.000043</td>\n",
-       "      <td>-0.000076</td>\n",
-       "      <td>-0.000072</td>\n",
-       "      <td>-0.000106</td>\n",
-       "      <td>0.000029</td>\n",
-       "      <td>-0.000055</td>\n",
-       "      <td>-0.000119</td>\n",
+       "      <td>-0.245506</td>\n",
+       "      <td>-0.190218</td>\n",
+       "      <td>-0.137243</td>\n",
+       "      <td>0.051545</td>\n",
+       "      <td>0.003048</td>\n",
+       "      <td>-0.128578</td>\n",
+       "      <td>-0.236718</td>\n",
+       "      <td>0.017593</td>\n",
+       "      <td>-0.094332</td>\n",
+       "      <td>-0.070269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2550,16 +3005,16 @@
        "      <td>163840</td>\n",
        "      <td>178946</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.002595</td>\n",
-       "      <td>-0.004256</td>\n",
-       "      <td>-0.002759</td>\n",
-       "      <td>-0.001601</td>\n",
-       "      <td>-0.002601</td>\n",
-       "      <td>-0.001238</td>\n",
-       "      <td>-0.001756</td>\n",
-       "      <td>-0.002918</td>\n",
-       "      <td>-0.001914</td>\n",
-       "      <td>-0.003244</td>\n",
+       "      <td>-0.005553</td>\n",
+       "      <td>-0.041326</td>\n",
+       "      <td>-0.028948</td>\n",
+       "      <td>0.001374</td>\n",
+       "      <td>0.048067</td>\n",
+       "      <td>-0.024705</td>\n",
+       "      <td>-0.086391</td>\n",
+       "      <td>0.017870</td>\n",
+       "      <td>-0.035195</td>\n",
+       "      <td>-0.059956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2574,16 +3029,16 @@
        "      <td>163840</td>\n",
        "      <td>178946</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000587</td>\n",
-       "      <td>-0.000894</td>\n",
-       "      <td>-0.000563</td>\n",
-       "      <td>-0.000324</td>\n",
-       "      <td>-0.000471</td>\n",
-       "      <td>-0.000258</td>\n",
-       "      <td>-0.000382</td>\n",
-       "      <td>-0.000683</td>\n",
-       "      <td>-0.000459</td>\n",
-       "      <td>-0.000757</td>\n",
+       "      <td>-0.241619</td>\n",
+       "      <td>-0.187318</td>\n",
+       "      <td>-0.135041</td>\n",
+       "      <td>0.050612</td>\n",
+       "      <td>0.005031</td>\n",
+       "      <td>-0.125195</td>\n",
+       "      <td>-0.233266</td>\n",
+       "      <td>0.019467</td>\n",
+       "      <td>-0.093385</td>\n",
+       "      <td>-0.068222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2598,16 +3053,16 @@
        "      <td>163840</td>\n",
        "      <td>170729</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.002933</td>\n",
-       "      <td>-0.004738</td>\n",
-       "      <td>-0.003541</td>\n",
-       "      <td>-0.001943</td>\n",
-       "      <td>-0.002958</td>\n",
-       "      <td>-0.001370</td>\n",
-       "      <td>-0.002272</td>\n",
-       "      <td>-0.003326</td>\n",
-       "      <td>-0.002195</td>\n",
-       "      <td>-0.003648</td>\n",
+       "      <td>0.152952</td>\n",
+       "      <td>0.291293</td>\n",
+       "      <td>-0.198963</td>\n",
+       "      <td>-0.430064</td>\n",
+       "      <td>0.270837</td>\n",
+       "      <td>-0.239649</td>\n",
+       "      <td>0.068286</td>\n",
+       "      <td>-0.406054</td>\n",
+       "      <td>-0.293787</td>\n",
+       "      <td>-0.101794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2646,16 +3101,16 @@
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.004391</td>\n",
-       "      <td>0.004506</td>\n",
-       "      <td>-0.017141</td>\n",
-       "      <td>0.000025</td>\n",
-       "      <td>0.042810</td>\n",
-       "      <td>0.024843</td>\n",
-       "      <td>-0.022073</td>\n",
-       "      <td>0.043973</td>\n",
-       "      <td>0.005934</td>\n",
-       "      <td>-0.006544</td>\n",
+       "      <td>-0.390896</td>\n",
+       "      <td>-0.056248</td>\n",
+       "      <td>-0.131846</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>0.052115</td>\n",
+       "      <td>-0.073442</td>\n",
+       "      <td>-0.171080</td>\n",
+       "      <td>0.151064</td>\n",
+       "      <td>-0.122629</td>\n",
+       "      <td>0.059758</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>78</th>\n",
@@ -2670,16 +3125,16 @@
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.129386</td>\n",
-       "      <td>-0.144098</td>\n",
-       "      <td>-0.077484</td>\n",
-       "      <td>-0.091391</td>\n",
-       "      <td>-0.104009</td>\n",
-       "      <td>-0.034113</td>\n",
-       "      <td>-0.096020</td>\n",
-       "      <td>-0.063028</td>\n",
-       "      <td>-0.079460</td>\n",
-       "      <td>-0.102111</td>\n",
+       "      <td>-0.026459</td>\n",
+       "      <td>-0.118378</td>\n",
+       "      <td>0.084991</td>\n",
+       "      <td>-0.022175</td>\n",
+       "      <td>-0.209899</td>\n",
+       "      <td>-0.091537</td>\n",
+       "      <td>0.038770</td>\n",
+       "      <td>-0.119916</td>\n",
+       "      <td>-0.181460</td>\n",
+       "      <td>-0.167976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
@@ -2694,16 +3149,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000218</td>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.000263</td>\n",
-       "      <td>0.000122</td>\n",
-       "      <td>0.000352</td>\n",
-       "      <td>0.000184</td>\n",
-       "      <td>0.000139</td>\n",
-       "      <td>0.000887</td>\n",
-       "      <td>0.000173</td>\n",
-       "      <td>0.000558</td>\n",
+       "      <td>-0.413859</td>\n",
+       "      <td>-0.029041</td>\n",
+       "      <td>-0.079324</td>\n",
+       "      <td>0.070361</td>\n",
+       "      <td>0.022809</td>\n",
+       "      <td>-0.047733</td>\n",
+       "      <td>-0.206144</td>\n",
+       "      <td>0.133108</td>\n",
+       "      <td>-0.046837</td>\n",
+       "      <td>-0.011002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80</th>\n",
@@ -2718,16 +3173,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000218</td>\n",
-       "      <td>-0.000109</td>\n",
-       "      <td>-0.000204</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>-0.000265</td>\n",
-       "      <td>-0.000104</td>\n",
-       "      <td>-0.000189</td>\n",
-       "      <td>0.000523</td>\n",
-       "      <td>-0.000149</td>\n",
-       "      <td>-0.000385</td>\n",
+       "      <td>-0.085005</td>\n",
+       "      <td>-0.115757</td>\n",
+       "      <td>-0.085665</td>\n",
+       "      <td>-0.038702</td>\n",
+       "      <td>0.006083</td>\n",
+       "      <td>-0.039524</td>\n",
+       "      <td>-0.157262</td>\n",
+       "      <td>-0.003096</td>\n",
+       "      <td>-0.075535</td>\n",
+       "      <td>-0.172014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>81</th>\n",
@@ -2742,16 +3197,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.001127</td>\n",
-       "      <td>-0.002115</td>\n",
-       "      <td>-0.001278</td>\n",
-       "      <td>-0.000581</td>\n",
-       "      <td>-0.001344</td>\n",
-       "      <td>-0.000724</td>\n",
-       "      <td>-0.000569</td>\n",
-       "      <td>-0.003553</td>\n",
-       "      <td>-0.000857</td>\n",
-       "      <td>-0.002281</td>\n",
+       "      <td>-0.223234</td>\n",
+       "      <td>-0.099899</td>\n",
+       "      <td>-0.119575</td>\n",
+       "      <td>0.070561</td>\n",
+       "      <td>-0.016501</td>\n",
+       "      <td>-0.110469</td>\n",
+       "      <td>-0.208272</td>\n",
+       "      <td>0.088579</td>\n",
+       "      <td>-0.018304</td>\n",
+       "      <td>0.014062</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2773,35 +3228,35 @@
        "81  chr1  109728807    T  GG    ALX3  109710224  110234512      -   \n",
        "\n",
        "    gene_mask_start  gene_mask_end  ...  agg_9528  agg_9529  agg_9530  \\\n",
-       "0            163840         172672  ... -0.003487 -0.006149 -0.003175   \n",
-       "1            163840         172672  ... -0.000096 -0.000190 -0.000135   \n",
-       "2            163840         178946  ... -0.002595 -0.004256 -0.002759   \n",
-       "3            163840         178946  ... -0.000587 -0.000894 -0.000563   \n",
-       "4            163840         170729  ... -0.002933 -0.004738 -0.003541   \n",
+       "0            163840         172672  ... -0.006633 -0.043378 -0.031300   \n",
+       "1            163840         172672  ... -0.245506 -0.190218 -0.137243   \n",
+       "2            163840         178946  ... -0.005553 -0.041326 -0.028948   \n",
+       "3            163840         178946  ... -0.241619 -0.187318 -0.135041   \n",
+       "4            163840         170729  ...  0.152952  0.291293 -0.198963   \n",
        "..              ...            ...  ...       ...       ...       ...   \n",
-       "77           163840         227488  ... -0.004391  0.004506 -0.017141   \n",
-       "78           163840         227488  ... -0.129386 -0.144098 -0.077484   \n",
-       "79           163840         174642  ...  0.000218  0.000532  0.000263   \n",
-       "80           163840         174642  ... -0.000218 -0.000109 -0.000204   \n",
-       "81           163840         174642  ... -0.001127 -0.002115 -0.001278   \n",
+       "77           163840         227488  ... -0.390896 -0.056248 -0.131846   \n",
+       "78           163840         227488  ... -0.026459 -0.118378  0.084991   \n",
+       "79           163840         174642  ... -0.413859 -0.029041 -0.079324   \n",
+       "80           163840         174642  ... -0.085005 -0.115757 -0.085665   \n",
+       "81           163840         174642  ... -0.223234 -0.099899 -0.119575   \n",
        "\n",
        "    agg_9531  agg_9532  agg_9533  agg_9535  agg_9536  agg_9537  agg_9538  \n",
-       "0  -0.002726 -0.003147 -0.001746 -0.002291 -0.005283 -0.003393 -0.006694  \n",
-       "1  -0.000043 -0.000076 -0.000072 -0.000106  0.000029 -0.000055 -0.000119  \n",
-       "2  -0.001601 -0.002601 -0.001238 -0.001756 -0.002918 -0.001914 -0.003244  \n",
-       "3  -0.000324 -0.000471 -0.000258 -0.000382 -0.000683 -0.000459 -0.000757  \n",
-       "4  -0.001943 -0.002958 -0.001370 -0.002272 -0.003326 -0.002195 -0.003648  \n",
+       "0  -0.002222  0.046180 -0.025395 -0.086669  0.013435 -0.037723 -0.062421  \n",
+       "1   0.051545  0.003048 -0.128578 -0.236718  0.017593 -0.094332 -0.070269  \n",
+       "2   0.001374  0.048067 -0.024705 -0.086391  0.017870 -0.035195 -0.059956  \n",
+       "3   0.050612  0.005031 -0.125195 -0.233266  0.019467 -0.093385 -0.068222  \n",
+       "4  -0.430064  0.270837 -0.239649  0.068286 -0.406054 -0.293787 -0.101794  \n",
        "..       ...       ...       ...       ...       ...       ...       ...  \n",
-       "77  0.000025  0.042810  0.024843 -0.022073  0.043973  0.005934 -0.006544  \n",
-       "78 -0.091391 -0.104009 -0.034113 -0.096020 -0.063028 -0.079460 -0.102111  \n",
-       "79  0.000122  0.000352  0.000184  0.000139  0.000887  0.000173  0.000558  \n",
-       "80  0.000009 -0.000265 -0.000104 -0.000189  0.000523 -0.000149 -0.000385  \n",
-       "81 -0.000581 -0.001344 -0.000724 -0.000569 -0.003553 -0.000857 -0.002281  \n",
+       "77  0.002266  0.052115 -0.073442 -0.171080  0.151064 -0.122629  0.059758  \n",
+       "78 -0.022175 -0.209899 -0.091537  0.038770 -0.119916 -0.181460 -0.167976  \n",
+       "79  0.070361  0.022809 -0.047733 -0.206144  0.133108 -0.046837 -0.011002  \n",
+       "80 -0.038702  0.006083 -0.039524 -0.157262 -0.003096 -0.075535 -0.172014  \n",
+       "81  0.070561 -0.016501 -0.110469 -0.208272  0.088579 -0.018304  0.014062  \n",
        "\n",
        "[82 rows x 8870 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2814,47 +3269,58 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or variant effect can be performed on vcf file."
+    "Or variant effect prediction can be performed on a vcf file."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1272.2MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.2 (1862.4MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1514.0MB/s)\n",
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib ...\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6c19dcc02b5f43c89ea51643583f352d",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/rich/live.py:260: UserWarning: \n",
+       "install \"ipywidgets\" for Jupyter support\n",
+       "</pre>\n"
+      ],
       "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+       "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/rich/live.py:260: UserWarning: \n",
+       "install \"ipywidgets\" for Jupyter support\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warnings:\n",
+      "allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file /home/lala8/.local/share/genomes/hg38/hg38.fa. If this is not expected, please check if you are using the correct genome version.\n"
+     ]
     }
    ],
    "source": [
@@ -2863,7 +3329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2924,16 +3390,16 @@
        "      <td>163840</td>\n",
        "      <td>172672</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000096</td>\n",
-       "      <td>-0.000190</td>\n",
-       "      <td>-0.000135</td>\n",
-       "      <td>-0.000043</td>\n",
-       "      <td>-0.000076</td>\n",
-       "      <td>-0.000072</td>\n",
-       "      <td>-0.000106</td>\n",
-       "      <td>0.000029</td>\n",
-       "      <td>-0.000055</td>\n",
-       "      <td>-0.000119</td>\n",
+       "      <td>-0.038816</td>\n",
+       "      <td>-0.132628</td>\n",
+       "      <td>0.012945</td>\n",
+       "      <td>-0.057034</td>\n",
+       "      <td>-0.045095</td>\n",
+       "      <td>-0.019845</td>\n",
+       "      <td>-0.048861</td>\n",
+       "      <td>-0.248287</td>\n",
+       "      <td>-0.157361</td>\n",
+       "      <td>-0.202191</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2948,16 +3414,16 @@
        "      <td>163840</td>\n",
        "      <td>178946</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000587</td>\n",
-       "      <td>-0.000894</td>\n",
-       "      <td>-0.000563</td>\n",
-       "      <td>-0.000324</td>\n",
-       "      <td>-0.000471</td>\n",
-       "      <td>-0.000258</td>\n",
-       "      <td>-0.000382</td>\n",
-       "      <td>-0.000683</td>\n",
-       "      <td>-0.000459</td>\n",
-       "      <td>-0.000757</td>\n",
+       "      <td>0.007422</td>\n",
+       "      <td>-0.040294</td>\n",
+       "      <td>0.037184</td>\n",
+       "      <td>-0.039102</td>\n",
+       "      <td>-0.069273</td>\n",
+       "      <td>-0.089758</td>\n",
+       "      <td>0.085668</td>\n",
+       "      <td>-0.100051</td>\n",
+       "      <td>0.199948</td>\n",
+       "      <td>-0.228135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2972,16 +3438,16 @@
        "      <td>163840</td>\n",
        "      <td>170729</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000845</td>\n",
-       "      <td>-0.001181</td>\n",
-       "      <td>-0.000784</td>\n",
-       "      <td>-0.000442</td>\n",
-       "      <td>-0.000589</td>\n",
-       "      <td>-0.000313</td>\n",
-       "      <td>-0.000526</td>\n",
-       "      <td>-0.000856</td>\n",
-       "      <td>-0.000637</td>\n",
-       "      <td>-0.000881</td>\n",
+       "      <td>-0.039081</td>\n",
+       "      <td>-0.132857</td>\n",
+       "      <td>0.012576</td>\n",
+       "      <td>-0.057286</td>\n",
+       "      <td>-0.044211</td>\n",
+       "      <td>-0.019335</td>\n",
+       "      <td>-0.049245</td>\n",
+       "      <td>-0.247860</td>\n",
+       "      <td>-0.158028</td>\n",
+       "      <td>-0.202599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2996,16 +3462,16 @@
        "      <td>163840</td>\n",
        "      <td>165050</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.001767</td>\n",
-       "      <td>-0.002189</td>\n",
-       "      <td>-0.001705</td>\n",
-       "      <td>-0.001091</td>\n",
-       "      <td>-0.001184</td>\n",
-       "      <td>-0.000717</td>\n",
-       "      <td>-0.001306</td>\n",
-       "      <td>-0.001550</td>\n",
-       "      <td>-0.001349</td>\n",
-       "      <td>-0.001550</td>\n",
+       "      <td>0.007090</td>\n",
+       "      <td>-0.040752</td>\n",
+       "      <td>0.036865</td>\n",
+       "      <td>-0.038709</td>\n",
+       "      <td>-0.069798</td>\n",
+       "      <td>-0.090022</td>\n",
+       "      <td>0.085745</td>\n",
+       "      <td>-0.100093</td>\n",
+       "      <td>0.199475</td>\n",
+       "      <td>-0.228633</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -3020,16 +3486,16 @@
        "      <td>163840</td>\n",
        "      <td>166306</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000098</td>\n",
-       "      <td>0.000052</td>\n",
-       "      <td>-0.000040</td>\n",
-       "      <td>-0.000051</td>\n",
-       "      <td>0.000037</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>-0.000024</td>\n",
-       "      <td>0.000093</td>\n",
-       "      <td>-0.000080</td>\n",
-       "      <td>-0.000203</td>\n",
+       "      <td>0.384038</td>\n",
+       "      <td>0.383892</td>\n",
+       "      <td>0.209059</td>\n",
+       "      <td>0.197611</td>\n",
+       "      <td>0.147189</td>\n",
+       "      <td>0.214235</td>\n",
+       "      <td>0.429188</td>\n",
+       "      <td>0.046692</td>\n",
+       "      <td>0.232973</td>\n",
+       "      <td>0.122444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -3044,16 +3510,16 @@
        "      <td>163840</td>\n",
        "      <td>167179</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000603</td>\n",
-       "      <td>-0.000860</td>\n",
-       "      <td>-0.000584</td>\n",
-       "      <td>-0.000328</td>\n",
-       "      <td>-0.000361</td>\n",
-       "      <td>-0.000211</td>\n",
-       "      <td>-0.000395</td>\n",
-       "      <td>-0.000428</td>\n",
-       "      <td>-0.000371</td>\n",
-       "      <td>-0.000468</td>\n",
+       "      <td>0.128468</td>\n",
+       "      <td>0.201122</td>\n",
+       "      <td>0.096130</td>\n",
+       "      <td>0.117577</td>\n",
+       "      <td>-0.126177</td>\n",
+       "      <td>-0.059776</td>\n",
+       "      <td>0.184826</td>\n",
+       "      <td>-0.009697</td>\n",
+       "      <td>0.305417</td>\n",
+       "      <td>-0.107728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -3068,16 +3534,16 @@
        "      <td>163840</td>\n",
        "      <td>198383</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000358</td>\n",
-       "      <td>-0.000528</td>\n",
-       "      <td>-0.000338</td>\n",
-       "      <td>-0.000193</td>\n",
-       "      <td>-0.000205</td>\n",
-       "      <td>-0.000113</td>\n",
-       "      <td>-0.000245</td>\n",
-       "      <td>-0.000265</td>\n",
-       "      <td>-0.000241</td>\n",
-       "      <td>-0.000347</td>\n",
+       "      <td>0.382609</td>\n",
+       "      <td>0.387782</td>\n",
+       "      <td>0.206949</td>\n",
+       "      <td>0.192670</td>\n",
+       "      <td>0.148521</td>\n",
+       "      <td>0.211755</td>\n",
+       "      <td>0.428720</td>\n",
+       "      <td>0.039945</td>\n",
+       "      <td>0.232015</td>\n",
+       "      <td>0.117866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -3092,16 +3558,16 @@
        "      <td>163840</td>\n",
        "      <td>184493</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001338</td>\n",
-       "      <td>0.000849</td>\n",
-       "      <td>0.000867</td>\n",
-       "      <td>0.000903</td>\n",
-       "      <td>0.001198</td>\n",
-       "      <td>0.001515</td>\n",
-       "      <td>0.001150</td>\n",
-       "      <td>0.001019</td>\n",
-       "      <td>0.000498</td>\n",
-       "      <td>0.001034</td>\n",
+       "      <td>0.127029</td>\n",
+       "      <td>0.199535</td>\n",
+       "      <td>0.092907</td>\n",
+       "      <td>0.114106</td>\n",
+       "      <td>-0.132683</td>\n",
+       "      <td>-0.060256</td>\n",
+       "      <td>0.180594</td>\n",
+       "      <td>-0.008448</td>\n",
+       "      <td>0.303062</td>\n",
+       "      <td>-0.112591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -3116,16 +3582,16 @@
        "      <td>163840</td>\n",
        "      <td>168975</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000518</td>\n",
-       "      <td>-0.000364</td>\n",
-       "      <td>-0.000522</td>\n",
-       "      <td>-0.000307</td>\n",
-       "      <td>-0.000214</td>\n",
-       "      <td>-0.000158</td>\n",
-       "      <td>-0.000330</td>\n",
-       "      <td>-0.000363</td>\n",
-       "      <td>-0.000348</td>\n",
-       "      <td>-0.000509</td>\n",
+       "      <td>-0.065035</td>\n",
+       "      <td>-0.009808</td>\n",
+       "      <td>-0.089304</td>\n",
+       "      <td>-0.123990</td>\n",
+       "      <td>-0.237687</td>\n",
+       "      <td>-0.145043</td>\n",
+       "      <td>-0.027265</td>\n",
+       "      <td>-0.172405</td>\n",
+       "      <td>-0.209000</td>\n",
+       "      <td>-0.169588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -3140,16 +3606,16 @@
        "      <td>163840</td>\n",
        "      <td>173223</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000109</td>\n",
-       "      <td>0.000011</td>\n",
-       "      <td>0.000054</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>-0.000056</td>\n",
-       "      <td>-0.000199</td>\n",
-       "      <td>-0.000207</td>\n",
-       "      <td>-0.000112</td>\n",
-       "      <td>0.000043</td>\n",
-       "      <td>0.000051</td>\n",
+       "      <td>0.149715</td>\n",
+       "      <td>0.036652</td>\n",
+       "      <td>0.236477</td>\n",
+       "      <td>-0.033082</td>\n",
+       "      <td>-0.059123</td>\n",
+       "      <td>-0.109544</td>\n",
+       "      <td>-0.116550</td>\n",
+       "      <td>-0.056748</td>\n",
+       "      <td>0.238428</td>\n",
+       "      <td>-0.066090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -3164,16 +3630,16 @@
        "      <td>163840</td>\n",
        "      <td>170339</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000278</td>\n",
-       "      <td>-0.000460</td>\n",
-       "      <td>-0.000305</td>\n",
-       "      <td>-0.000142</td>\n",
-       "      <td>-0.000220</td>\n",
-       "      <td>-0.000106</td>\n",
-       "      <td>-0.000170</td>\n",
-       "      <td>-0.000272</td>\n",
-       "      <td>-0.000170</td>\n",
-       "      <td>-0.000231</td>\n",
+       "      <td>-0.064055</td>\n",
+       "      <td>-0.008839</td>\n",
+       "      <td>-0.088600</td>\n",
+       "      <td>-0.123287</td>\n",
+       "      <td>-0.236878</td>\n",
+       "      <td>-0.144462</td>\n",
+       "      <td>-0.026132</td>\n",
+       "      <td>-0.171725</td>\n",
+       "      <td>-0.208052</td>\n",
+       "      <td>-0.169746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -3188,16 +3654,16 @@
        "      <td>163840</td>\n",
        "      <td>177242</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.007892</td>\n",
-       "      <td>0.005266</td>\n",
-       "      <td>0.008324</td>\n",
-       "      <td>0.007764</td>\n",
-       "      <td>-0.001580</td>\n",
-       "      <td>0.002468</td>\n",
-       "      <td>0.008656</td>\n",
-       "      <td>0.002925</td>\n",
-       "      <td>0.007485</td>\n",
-       "      <td>0.010022</td>\n",
+       "      <td>0.148965</td>\n",
+       "      <td>0.036048</td>\n",
+       "      <td>0.236015</td>\n",
+       "      <td>-0.033107</td>\n",
+       "      <td>-0.059445</td>\n",
+       "      <td>-0.110096</td>\n",
+       "      <td>-0.117363</td>\n",
+       "      <td>-0.057286</td>\n",
+       "      <td>0.237902</td>\n",
+       "      <td>-0.067406</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
@@ -3212,16 +3678,16 @@
        "      <td>163840</td>\n",
        "      <td>166924</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000308</td>\n",
-       "      <td>-0.000433</td>\n",
-       "      <td>-0.000336</td>\n",
-       "      <td>-0.000197</td>\n",
-       "      <td>-0.000195</td>\n",
-       "      <td>-0.000144</td>\n",
-       "      <td>-0.000220</td>\n",
-       "      <td>-0.000190</td>\n",
-       "      <td>-0.000199</td>\n",
-       "      <td>-0.000302</td>\n",
+       "      <td>0.025167</td>\n",
+       "      <td>0.316047</td>\n",
+       "      <td>0.052610</td>\n",
+       "      <td>-0.128209</td>\n",
+       "      <td>-0.025701</td>\n",
+       "      <td>0.018469</td>\n",
+       "      <td>0.071030</td>\n",
+       "      <td>0.035337</td>\n",
+       "      <td>0.035106</td>\n",
+       "      <td>0.198629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -3236,16 +3702,16 @@
        "      <td>163840</td>\n",
        "      <td>166653</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000255</td>\n",
-       "      <td>-0.000385</td>\n",
-       "      <td>-0.000261</td>\n",
-       "      <td>-0.000158</td>\n",
-       "      <td>-0.000164</td>\n",
-       "      <td>-0.000123</td>\n",
-       "      <td>-0.000158</td>\n",
-       "      <td>-0.000281</td>\n",
-       "      <td>-0.000191</td>\n",
-       "      <td>-0.000319</td>\n",
+       "      <td>-0.060978</td>\n",
+       "      <td>0.136151</td>\n",
+       "      <td>0.222250</td>\n",
+       "      <td>0.180415</td>\n",
+       "      <td>-0.077709</td>\n",
+       "      <td>0.059230</td>\n",
+       "      <td>-0.152584</td>\n",
+       "      <td>0.021212</td>\n",
+       "      <td>0.387905</td>\n",
+       "      <td>0.087836</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
@@ -3260,16 +3726,16 @@
        "      <td>163840</td>\n",
        "      <td>199838</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000901</td>\n",
-       "      <td>-0.001054</td>\n",
-       "      <td>-0.000771</td>\n",
-       "      <td>-0.001977</td>\n",
-       "      <td>-0.000437</td>\n",
-       "      <td>-0.000555</td>\n",
-       "      <td>-0.001084</td>\n",
-       "      <td>-0.000583</td>\n",
-       "      <td>-0.001613</td>\n",
-       "      <td>-0.000536</td>\n",
+       "      <td>0.024651</td>\n",
+       "      <td>0.315260</td>\n",
+       "      <td>0.050833</td>\n",
+       "      <td>-0.121852</td>\n",
+       "      <td>-0.024862</td>\n",
+       "      <td>0.020706</td>\n",
+       "      <td>0.065433</td>\n",
+       "      <td>0.039559</td>\n",
+       "      <td>0.038879</td>\n",
+       "      <td>0.207624</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
@@ -3284,16 +3750,16 @@
        "      <td>163840</td>\n",
        "      <td>178999</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000146</td>\n",
-       "      <td>-0.000230</td>\n",
-       "      <td>-0.000180</td>\n",
-       "      <td>-0.000099</td>\n",
-       "      <td>-0.000132</td>\n",
-       "      <td>-0.000082</td>\n",
-       "      <td>-0.000092</td>\n",
-       "      <td>-0.000189</td>\n",
-       "      <td>-0.000112</td>\n",
-       "      <td>-0.000195</td>\n",
+       "      <td>-0.069684</td>\n",
+       "      <td>0.130147</td>\n",
+       "      <td>0.214494</td>\n",
+       "      <td>0.163851</td>\n",
+       "      <td>-0.088984</td>\n",
+       "      <td>0.049194</td>\n",
+       "      <td>-0.161672</td>\n",
+       "      <td>0.012433</td>\n",
+       "      <td>0.376862</td>\n",
+       "      <td>0.079508</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
@@ -3308,16 +3774,16 @@
        "      <td>163840</td>\n",
        "      <td>168116</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000458</td>\n",
-       "      <td>-0.000697</td>\n",
-       "      <td>-0.000491</td>\n",
-       "      <td>-0.000359</td>\n",
-       "      <td>-0.000367</td>\n",
-       "      <td>-0.000241</td>\n",
-       "      <td>-0.000268</td>\n",
-       "      <td>-0.000487</td>\n",
-       "      <td>-0.000344</td>\n",
-       "      <td>-0.000595</td>\n",
+       "      <td>-0.068721</td>\n",
+       "      <td>-0.131200</td>\n",
+       "      <td>-0.095311</td>\n",
+       "      <td>0.046062</td>\n",
+       "      <td>0.058227</td>\n",
+       "      <td>-0.014569</td>\n",
+       "      <td>-0.110806</td>\n",
+       "      <td>0.156508</td>\n",
+       "      <td>0.050760</td>\n",
+       "      <td>0.086102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
@@ -3332,16 +3798,16 @@
        "      <td>163840</td>\n",
        "      <td>183816</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000815</td>\n",
-       "      <td>-0.001141</td>\n",
-       "      <td>-0.000897</td>\n",
-       "      <td>-0.000674</td>\n",
-       "      <td>-0.000717</td>\n",
-       "      <td>-0.000515</td>\n",
-       "      <td>-0.000520</td>\n",
-       "      <td>-0.000780</td>\n",
-       "      <td>-0.000614</td>\n",
-       "      <td>-0.001010</td>\n",
+       "      <td>-0.000722</td>\n",
+       "      <td>-0.065477</td>\n",
+       "      <td>-0.015448</td>\n",
+       "      <td>-0.048512</td>\n",
+       "      <td>0.080478</td>\n",
+       "      <td>0.044108</td>\n",
+       "      <td>-0.040190</td>\n",
+       "      <td>-0.109651</td>\n",
+       "      <td>0.016822</td>\n",
+       "      <td>-0.062620</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
@@ -3356,16 +3822,16 @@
        "      <td>163840</td>\n",
        "      <td>181059</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000748</td>\n",
-       "      <td>-0.001163</td>\n",
-       "      <td>-0.000843</td>\n",
-       "      <td>-0.000615</td>\n",
-       "      <td>-0.000704</td>\n",
-       "      <td>-0.000456</td>\n",
-       "      <td>-0.000404</td>\n",
-       "      <td>-0.000761</td>\n",
-       "      <td>-0.000572</td>\n",
-       "      <td>-0.000986</td>\n",
+       "      <td>-0.068239</td>\n",
+       "      <td>-0.130473</td>\n",
+       "      <td>-0.095077</td>\n",
+       "      <td>0.046099</td>\n",
+       "      <td>0.058977</td>\n",
+       "      <td>-0.014226</td>\n",
+       "      <td>-0.110129</td>\n",
+       "      <td>0.156933</td>\n",
+       "      <td>0.051034</td>\n",
+       "      <td>0.086115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
@@ -3380,16 +3846,16 @@
        "      <td>163840</td>\n",
        "      <td>176946</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000021</td>\n",
-       "      <td>0.000008</td>\n",
-       "      <td>0.000037</td>\n",
-       "      <td>0.000035</td>\n",
-       "      <td>0.000096</td>\n",
-       "      <td>0.000057</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>0.000184</td>\n",
-       "      <td>0.000039</td>\n",
-       "      <td>0.000044</td>\n",
+       "      <td>-0.000799</td>\n",
+       "      <td>-0.065457</td>\n",
+       "      <td>-0.015547</td>\n",
+       "      <td>-0.048479</td>\n",
+       "      <td>0.080698</td>\n",
+       "      <td>0.044615</td>\n",
+       "      <td>-0.040375</td>\n",
+       "      <td>-0.109146</td>\n",
+       "      <td>0.016773</td>\n",
+       "      <td>-0.063049</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
@@ -3404,16 +3870,16 @@
        "      <td>163840</td>\n",
        "      <td>177982</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000325</td>\n",
-       "      <td>-0.000491</td>\n",
-       "      <td>-0.000359</td>\n",
-       "      <td>-0.000260</td>\n",
-       "      <td>-0.000322</td>\n",
-       "      <td>-0.000207</td>\n",
-       "      <td>-0.000179</td>\n",
-       "      <td>-0.000336</td>\n",
-       "      <td>-0.000235</td>\n",
-       "      <td>-0.000382</td>\n",
+       "      <td>0.144724</td>\n",
+       "      <td>0.096923</td>\n",
+       "      <td>0.053529</td>\n",
+       "      <td>0.178255</td>\n",
+       "      <td>0.186835</td>\n",
+       "      <td>-0.086366</td>\n",
+       "      <td>0.078235</td>\n",
+       "      <td>0.339634</td>\n",
+       "      <td>0.164180</td>\n",
+       "      <td>0.351917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
@@ -3428,16 +3894,16 @@
        "      <td>163840</td>\n",
        "      <td>172928</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000077</td>\n",
-       "      <td>-0.000098</td>\n",
-       "      <td>-0.000090</td>\n",
-       "      <td>-0.000067</td>\n",
-       "      <td>-0.000092</td>\n",
-       "      <td>-0.000057</td>\n",
-       "      <td>-0.000034</td>\n",
-       "      <td>-0.000212</td>\n",
-       "      <td>-0.000063</td>\n",
-       "      <td>-0.000159</td>\n",
+       "      <td>-0.204588</td>\n",
+       "      <td>-0.164875</td>\n",
+       "      <td>-0.161451</td>\n",
+       "      <td>-0.077614</td>\n",
+       "      <td>0.001604</td>\n",
+       "      <td>-0.079316</td>\n",
+       "      <td>-0.283131</td>\n",
+       "      <td>-0.060867</td>\n",
+       "      <td>-0.083952</td>\n",
+       "      <td>-0.061946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
@@ -3452,16 +3918,16 @@
        "      <td>163840</td>\n",
        "      <td>180515</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000103</td>\n",
-       "      <td>0.000070</td>\n",
-       "      <td>0.000060</td>\n",
-       "      <td>0.000038</td>\n",
-       "      <td>0.000027</td>\n",
-       "      <td>0.000055</td>\n",
-       "      <td>0.000078</td>\n",
-       "      <td>0.000277</td>\n",
-       "      <td>0.000062</td>\n",
-       "      <td>0.000245</td>\n",
+       "      <td>0.145371</td>\n",
+       "      <td>0.099419</td>\n",
+       "      <td>0.055633</td>\n",
+       "      <td>0.177352</td>\n",
+       "      <td>0.190575</td>\n",
+       "      <td>-0.084158</td>\n",
+       "      <td>0.083270</td>\n",
+       "      <td>0.344398</td>\n",
+       "      <td>0.166967</td>\n",
+       "      <td>0.355001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
@@ -3476,16 +3942,16 @@
        "      <td>163840</td>\n",
        "      <td>180515</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.003317</td>\n",
-       "      <td>0.005164</td>\n",
-       "      <td>0.002698</td>\n",
-       "      <td>0.001438</td>\n",
-       "      <td>0.001558</td>\n",
-       "      <td>0.001273</td>\n",
-       "      <td>0.001937</td>\n",
-       "      <td>0.005484</td>\n",
-       "      <td>0.002190</td>\n",
-       "      <td>0.005321</td>\n",
+       "      <td>-0.216764</td>\n",
+       "      <td>-0.175109</td>\n",
+       "      <td>-0.172126</td>\n",
+       "      <td>-0.086336</td>\n",
+       "      <td>-0.018204</td>\n",
+       "      <td>-0.098414</td>\n",
+       "      <td>-0.295143</td>\n",
+       "      <td>-0.081368</td>\n",
+       "      <td>-0.096516</td>\n",
+       "      <td>-0.078732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
@@ -3500,16 +3966,16 @@
        "      <td>163840</td>\n",
        "      <td>179428</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.002225</td>\n",
-       "      <td>0.001688</td>\n",
-       "      <td>0.001366</td>\n",
-       "      <td>0.001251</td>\n",
-       "      <td>0.001342</td>\n",
-       "      <td>0.000177</td>\n",
-       "      <td>0.001402</td>\n",
-       "      <td>0.000480</td>\n",
-       "      <td>0.001663</td>\n",
-       "      <td>0.001764</td>\n",
+       "      <td>0.125082</td>\n",
+       "      <td>0.187445</td>\n",
+       "      <td>0.094098</td>\n",
+       "      <td>0.073541</td>\n",
+       "      <td>0.153524</td>\n",
+       "      <td>0.122184</td>\n",
+       "      <td>0.178186</td>\n",
+       "      <td>0.325020</td>\n",
+       "      <td>0.131711</td>\n",
+       "      <td>0.251710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
@@ -3524,16 +3990,16 @@
        "      <td>163840</td>\n",
        "      <td>179428</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.004184</td>\n",
-       "      <td>-0.003700</td>\n",
-       "      <td>-0.003180</td>\n",
-       "      <td>-0.004169</td>\n",
-       "      <td>-0.003774</td>\n",
-       "      <td>-0.003399</td>\n",
-       "      <td>-0.003382</td>\n",
-       "      <td>0.000949</td>\n",
-       "      <td>-0.002237</td>\n",
-       "      <td>-0.002981</td>\n",
+       "      <td>-0.072443</td>\n",
+       "      <td>-0.081550</td>\n",
+       "      <td>-0.038486</td>\n",
+       "      <td>-0.047714</td>\n",
+       "      <td>0.226176</td>\n",
+       "      <td>0.045261</td>\n",
+       "      <td>-0.126760</td>\n",
+       "      <td>0.371777</td>\n",
+       "      <td>0.060598</td>\n",
+       "      <td>-0.005164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
@@ -3548,16 +4014,16 @@
        "      <td>163840</td>\n",
        "      <td>173165</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000055</td>\n",
-       "      <td>-0.000036</td>\n",
-       "      <td>0.000088</td>\n",
-       "      <td>-0.000085</td>\n",
-       "      <td>0.000037</td>\n",
-       "      <td>-0.000040</td>\n",
-       "      <td>-0.000056</td>\n",
-       "      <td>-0.000044</td>\n",
-       "      <td>-0.000046</td>\n",
-       "      <td>-0.000027</td>\n",
+       "      <td>0.125229</td>\n",
+       "      <td>0.187538</td>\n",
+       "      <td>0.094372</td>\n",
+       "      <td>0.073884</td>\n",
+       "      <td>0.154733</td>\n",
+       "      <td>0.122949</td>\n",
+       "      <td>0.178414</td>\n",
+       "      <td>0.326226</td>\n",
+       "      <td>0.132067</td>\n",
+       "      <td>0.251894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
@@ -3572,16 +4038,16 @@
        "      <td>163840</td>\n",
        "      <td>173165</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000644</td>\n",
-       "      <td>-0.000063</td>\n",
-       "      <td>0.001706</td>\n",
-       "      <td>0.001486</td>\n",
-       "      <td>0.003162</td>\n",
-       "      <td>0.001671</td>\n",
-       "      <td>0.000299</td>\n",
-       "      <td>0.002102</td>\n",
-       "      <td>0.001007</td>\n",
-       "      <td>0.001831</td>\n",
+       "      <td>-0.072734</td>\n",
+       "      <td>-0.082032</td>\n",
+       "      <td>-0.038806</td>\n",
+       "      <td>-0.047959</td>\n",
+       "      <td>0.226084</td>\n",
+       "      <td>0.045132</td>\n",
+       "      <td>-0.126927</td>\n",
+       "      <td>0.371727</td>\n",
+       "      <td>0.060446</td>\n",
+       "      <td>-0.005538</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
@@ -3596,16 +4062,16 @@
        "      <td>163840</td>\n",
        "      <td>172720</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000533</td>\n",
-       "      <td>0.000539</td>\n",
-       "      <td>0.000585</td>\n",
-       "      <td>0.000474</td>\n",
-       "      <td>0.000076</td>\n",
-       "      <td>0.000280</td>\n",
-       "      <td>0.000422</td>\n",
-       "      <td>-0.000033</td>\n",
-       "      <td>0.000436</td>\n",
-       "      <td>0.000369</td>\n",
+       "      <td>-0.109239</td>\n",
+       "      <td>-0.095734</td>\n",
+       "      <td>-0.046736</td>\n",
+       "      <td>-0.160156</td>\n",
+       "      <td>-0.108054</td>\n",
+       "      <td>-0.116374</td>\n",
+       "      <td>0.049736</td>\n",
+       "      <td>0.170926</td>\n",
+       "      <td>0.112000</td>\n",
+       "      <td>0.250690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
@@ -3620,16 +4086,16 @@
        "      <td>163840</td>\n",
        "      <td>172720</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.003902</td>\n",
-       "      <td>0.002963</td>\n",
-       "      <td>0.001609</td>\n",
-       "      <td>0.002841</td>\n",
-       "      <td>0.000024</td>\n",
-       "      <td>0.000726</td>\n",
-       "      <td>0.001582</td>\n",
-       "      <td>0.000497</td>\n",
-       "      <td>0.002237</td>\n",
-       "      <td>-0.000092</td>\n",
+       "      <td>-0.014338</td>\n",
+       "      <td>-0.047306</td>\n",
+       "      <td>0.040593</td>\n",
+       "      <td>0.034276</td>\n",
+       "      <td>0.086477</td>\n",
+       "      <td>-0.002776</td>\n",
+       "      <td>-0.036525</td>\n",
+       "      <td>0.105931</td>\n",
+       "      <td>0.075943</td>\n",
+       "      <td>-0.094462</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
@@ -3644,16 +4110,16 @@
        "      <td>163840</td>\n",
        "      <td>172374</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000144</td>\n",
-       "      <td>0.000244</td>\n",
-       "      <td>0.000101</td>\n",
-       "      <td>0.000130</td>\n",
-       "      <td>-0.000119</td>\n",
-       "      <td>-0.000066</td>\n",
-       "      <td>0.000112</td>\n",
-       "      <td>0.000059</td>\n",
-       "      <td>0.000113</td>\n",
-       "      <td>0.000135</td>\n",
+       "      <td>-0.114962</td>\n",
+       "      <td>-0.101682</td>\n",
+       "      <td>-0.051288</td>\n",
+       "      <td>-0.167562</td>\n",
+       "      <td>-0.114795</td>\n",
+       "      <td>-0.118255</td>\n",
+       "      <td>0.045381</td>\n",
+       "      <td>0.164219</td>\n",
+       "      <td>0.106147</td>\n",
+       "      <td>0.242618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
@@ -3668,16 +4134,16 @@
        "      <td>163840</td>\n",
        "      <td>172374</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000287</td>\n",
-       "      <td>0.000869</td>\n",
-       "      <td>0.000338</td>\n",
-       "      <td>0.000516</td>\n",
-       "      <td>0.000702</td>\n",
-       "      <td>0.000260</td>\n",
-       "      <td>0.000547</td>\n",
-       "      <td>0.000815</td>\n",
-       "      <td>0.000190</td>\n",
-       "      <td>0.000561</td>\n",
+       "      <td>-0.014739</td>\n",
+       "      <td>-0.051509</td>\n",
+       "      <td>0.039989</td>\n",
+       "      <td>0.034758</td>\n",
+       "      <td>0.078923</td>\n",
+       "      <td>-0.006194</td>\n",
+       "      <td>-0.037574</td>\n",
+       "      <td>0.102354</td>\n",
+       "      <td>0.074973</td>\n",
+       "      <td>-0.093299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
@@ -3692,16 +4158,16 @@
        "      <td>163840</td>\n",
        "      <td>170946</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000685</td>\n",
-       "      <td>-0.000671</td>\n",
-       "      <td>-0.000490</td>\n",
-       "      <td>-0.000305</td>\n",
-       "      <td>-0.000203</td>\n",
-       "      <td>-0.000045</td>\n",
-       "      <td>-0.000183</td>\n",
-       "      <td>-0.000423</td>\n",
-       "      <td>-0.000412</td>\n",
-       "      <td>-0.000544</td>\n",
+       "      <td>0.017597</td>\n",
+       "      <td>-0.080504</td>\n",
+       "      <td>0.049995</td>\n",
+       "      <td>-0.011808</td>\n",
+       "      <td>-0.271627</td>\n",
+       "      <td>-0.061634</td>\n",
+       "      <td>-0.068092</td>\n",
+       "      <td>-0.169892</td>\n",
+       "      <td>-0.139209</td>\n",
+       "      <td>-0.134266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
@@ -3716,16 +4182,16 @@
        "      <td>163840</td>\n",
        "      <td>170946</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.014859</td>\n",
-       "      <td>0.029183</td>\n",
-       "      <td>0.015364</td>\n",
-       "      <td>0.007996</td>\n",
-       "      <td>0.015255</td>\n",
-       "      <td>0.007950</td>\n",
-       "      <td>0.010278</td>\n",
-       "      <td>0.021851</td>\n",
-       "      <td>0.009230</td>\n",
-       "      <td>0.024402</td>\n",
+       "      <td>-0.297318</td>\n",
+       "      <td>0.036640</td>\n",
+       "      <td>0.039409</td>\n",
+       "      <td>0.172793</td>\n",
+       "      <td>0.131960</td>\n",
+       "      <td>0.099102</td>\n",
+       "      <td>-0.127269</td>\n",
+       "      <td>0.248293</td>\n",
+       "      <td>0.078142</td>\n",
+       "      <td>0.244484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
@@ -3740,16 +4206,16 @@
        "      <td>163840</td>\n",
        "      <td>233549</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.002294</td>\n",
-       "      <td>0.002954</td>\n",
-       "      <td>0.003762</td>\n",
-       "      <td>0.001515</td>\n",
-       "      <td>-0.001879</td>\n",
-       "      <td>-0.003638</td>\n",
-       "      <td>0.004167</td>\n",
-       "      <td>-0.000977</td>\n",
-       "      <td>0.003568</td>\n",
-       "      <td>0.001709</td>\n",
+       "      <td>0.016951</td>\n",
+       "      <td>-0.081113</td>\n",
+       "      <td>0.048497</td>\n",
+       "      <td>-0.012860</td>\n",
+       "      <td>-0.272076</td>\n",
+       "      <td>-0.061712</td>\n",
+       "      <td>-0.068612</td>\n",
+       "      <td>-0.168814</td>\n",
+       "      <td>-0.140736</td>\n",
+       "      <td>-0.135443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
@@ -3764,16 +4230,16 @@
        "      <td>163840</td>\n",
        "      <td>233549</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.002409</td>\n",
-       "      <td>-0.002181</td>\n",
-       "      <td>0.001515</td>\n",
-       "      <td>0.007504</td>\n",
-       "      <td>0.011276</td>\n",
-       "      <td>0.019385</td>\n",
-       "      <td>-0.002945</td>\n",
-       "      <td>0.020405</td>\n",
-       "      <td>-0.001569</td>\n",
-       "      <td>0.007003</td>\n",
+       "      <td>-0.296372</td>\n",
+       "      <td>0.026870</td>\n",
+       "      <td>0.034203</td>\n",
+       "      <td>0.171992</td>\n",
+       "      <td>0.132727</td>\n",
+       "      <td>0.100970</td>\n",
+       "      <td>-0.133759</td>\n",
+       "      <td>0.247039</td>\n",
+       "      <td>0.073710</td>\n",
+       "      <td>0.242920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
@@ -3788,16 +4254,16 @@
        "      <td>163840</td>\n",
        "      <td>179789</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000565</td>\n",
-       "      <td>0.001265</td>\n",
-       "      <td>0.001679</td>\n",
-       "      <td>-0.000034</td>\n",
-       "      <td>-0.002686</td>\n",
-       "      <td>-0.000878</td>\n",
-       "      <td>0.001329</td>\n",
-       "      <td>-0.001112</td>\n",
-       "      <td>0.002059</td>\n",
-       "      <td>0.000288</td>\n",
+       "      <td>0.172656</td>\n",
+       "      <td>0.238587</td>\n",
+       "      <td>-0.112662</td>\n",
+       "      <td>-0.397093</td>\n",
+       "      <td>-0.048434</td>\n",
+       "      <td>-0.349497</td>\n",
+       "      <td>0.178258</td>\n",
+       "      <td>-0.541721</td>\n",
+       "      <td>-0.432872</td>\n",
+       "      <td>-0.115762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
@@ -3812,16 +4278,16 @@
        "      <td>163840</td>\n",
        "      <td>179789</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.015521</td>\n",
-       "      <td>0.005221</td>\n",
-       "      <td>0.011660</td>\n",
-       "      <td>0.028807</td>\n",
-       "      <td>0.010368</td>\n",
-       "      <td>0.014050</td>\n",
-       "      <td>0.007810</td>\n",
-       "      <td>0.026553</td>\n",
-       "      <td>0.009914</td>\n",
-       "      <td>0.026500</td>\n",
+       "      <td>-0.587357</td>\n",
+       "      <td>-0.383792</td>\n",
+       "      <td>-0.075256</td>\n",
+       "      <td>0.116061</td>\n",
+       "      <td>-0.107381</td>\n",
+       "      <td>-0.537354</td>\n",
+       "      <td>-0.290173</td>\n",
+       "      <td>0.274848</td>\n",
+       "      <td>-0.387343</td>\n",
+       "      <td>-0.475388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
@@ -3836,16 +4302,16 @@
        "      <td>163840</td>\n",
        "      <td>182577</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000140</td>\n",
-       "      <td>0.001370</td>\n",
-       "      <td>0.000755</td>\n",
-       "      <td>0.000528</td>\n",
-       "      <td>-0.000446</td>\n",
-       "      <td>-0.000184</td>\n",
-       "      <td>-0.001062</td>\n",
-       "      <td>0.000593</td>\n",
-       "      <td>0.000575</td>\n",
-       "      <td>0.002940</td>\n",
+       "      <td>0.196620</td>\n",
+       "      <td>0.244671</td>\n",
+       "      <td>-0.095485</td>\n",
+       "      <td>-0.382966</td>\n",
+       "      <td>-0.053531</td>\n",
+       "      <td>-0.346830</td>\n",
+       "      <td>0.205384</td>\n",
+       "      <td>-0.547652</td>\n",
+       "      <td>-0.427548</td>\n",
+       "      <td>-0.109037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
@@ -3860,16 +4326,16 @@
        "      <td>163840</td>\n",
        "      <td>182577</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.032480</td>\n",
-       "      <td>-0.033616</td>\n",
-       "      <td>-0.029502</td>\n",
-       "      <td>-0.050562</td>\n",
-       "      <td>-0.030190</td>\n",
-       "      <td>-0.021981</td>\n",
-       "      <td>-0.012192</td>\n",
-       "      <td>-0.029633</td>\n",
-       "      <td>-0.037296</td>\n",
-       "      <td>-0.067220</td>\n",
+       "      <td>-0.598168</td>\n",
+       "      <td>-0.350979</td>\n",
+       "      <td>-0.061809</td>\n",
+       "      <td>0.137094</td>\n",
+       "      <td>-0.114290</td>\n",
+       "      <td>-0.549722</td>\n",
+       "      <td>-0.316452</td>\n",
+       "      <td>0.281865</td>\n",
+       "      <td>-0.367328</td>\n",
+       "      <td>-0.457750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
@@ -3884,16 +4350,16 @@
        "      <td>163840</td>\n",
        "      <td>205369</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.001281</td>\n",
-       "      <td>-0.001583</td>\n",
-       "      <td>-0.000117</td>\n",
-       "      <td>-0.000501</td>\n",
-       "      <td>-0.001837</td>\n",
-       "      <td>-0.001011</td>\n",
-       "      <td>-0.002222</td>\n",
-       "      <td>0.000031</td>\n",
-       "      <td>0.000068</td>\n",
-       "      <td>0.000062</td>\n",
+       "      <td>-0.065015</td>\n",
+       "      <td>-0.134536</td>\n",
+       "      <td>-0.140023</td>\n",
+       "      <td>0.024319</td>\n",
+       "      <td>-0.060507</td>\n",
+       "      <td>-0.108153</td>\n",
+       "      <td>-0.149791</td>\n",
+       "      <td>-0.038775</td>\n",
+       "      <td>-0.076944</td>\n",
+       "      <td>-0.118178</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
@@ -3908,16 +4374,16 @@
        "      <td>163840</td>\n",
        "      <td>205369</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.006389</td>\n",
-       "      <td>0.010391</td>\n",
-       "      <td>0.008292</td>\n",
-       "      <td>0.010647</td>\n",
-       "      <td>-0.005511</td>\n",
-       "      <td>0.002638</td>\n",
-       "      <td>-0.004853</td>\n",
-       "      <td>-0.009829</td>\n",
-       "      <td>0.008542</td>\n",
-       "      <td>0.011590</td>\n",
+       "      <td>-0.138112</td>\n",
+       "      <td>-0.135524</td>\n",
+       "      <td>-0.122887</td>\n",
+       "      <td>0.003460</td>\n",
+       "      <td>-0.095921</td>\n",
+       "      <td>-0.142851</td>\n",
+       "      <td>-0.133408</td>\n",
+       "      <td>-0.044298</td>\n",
+       "      <td>-0.079864</td>\n",
+       "      <td>0.063350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
@@ -3932,16 +4398,16 @@
        "      <td>163840</td>\n",
        "      <td>185065</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001077</td>\n",
-       "      <td>0.002305</td>\n",
-       "      <td>0.001138</td>\n",
-       "      <td>-0.000056</td>\n",
-       "      <td>-0.000738</td>\n",
-       "      <td>0.000059</td>\n",
-       "      <td>0.000456</td>\n",
-       "      <td>-0.000396</td>\n",
-       "      <td>0.000904</td>\n",
-       "      <td>0.000512</td>\n",
+       "      <td>-0.064923</td>\n",
+       "      <td>-0.134493</td>\n",
+       "      <td>-0.140439</td>\n",
+       "      <td>0.024307</td>\n",
+       "      <td>-0.060060</td>\n",
+       "      <td>-0.106959</td>\n",
+       "      <td>-0.150438</td>\n",
+       "      <td>-0.038277</td>\n",
+       "      <td>-0.077278</td>\n",
+       "      <td>-0.118272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
@@ -3956,16 +4422,16 @@
        "      <td>163840</td>\n",
        "      <td>185065</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.009587</td>\n",
-       "      <td>0.012155</td>\n",
-       "      <td>0.002769</td>\n",
-       "      <td>0.018908</td>\n",
-       "      <td>0.010602</td>\n",
-       "      <td>0.000302</td>\n",
-       "      <td>0.003231</td>\n",
-       "      <td>0.007630</td>\n",
-       "      <td>0.011715</td>\n",
-       "      <td>0.019595</td>\n",
+       "      <td>-0.138915</td>\n",
+       "      <td>-0.137064</td>\n",
+       "      <td>-0.123798</td>\n",
+       "      <td>0.002397</td>\n",
+       "      <td>-0.096466</td>\n",
+       "      <td>-0.143198</td>\n",
+       "      <td>-0.133723</td>\n",
+       "      <td>-0.044947</td>\n",
+       "      <td>-0.080987</td>\n",
+       "      <td>0.062265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
@@ -3980,16 +4446,16 @@
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001978</td>\n",
-       "      <td>0.001395</td>\n",
-       "      <td>0.001501</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>-0.000538</td>\n",
-       "      <td>-0.000914</td>\n",
-       "      <td>0.002817</td>\n",
-       "      <td>-0.001998</td>\n",
-       "      <td>0.001149</td>\n",
-       "      <td>0.002069</td>\n",
+       "      <td>-0.260050</td>\n",
+       "      <td>-0.168407</td>\n",
+       "      <td>-0.271245</td>\n",
+       "      <td>-0.247335</td>\n",
+       "      <td>-0.234919</td>\n",
+       "      <td>-0.383042</td>\n",
+       "      <td>-0.163728</td>\n",
+       "      <td>-0.055309</td>\n",
+       "      <td>-0.088513</td>\n",
+       "      <td>-0.155641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
@@ -4004,16 +4470,16 @@
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.004803</td>\n",
-       "      <td>0.004060</td>\n",
-       "      <td>-0.018828</td>\n",
-       "      <td>-0.000383</td>\n",
-       "      <td>0.042374</td>\n",
-       "      <td>0.022364</td>\n",
-       "      <td>-0.023450</td>\n",
-       "      <td>0.043353</td>\n",
-       "      <td>0.006033</td>\n",
-       "      <td>-0.006614</td>\n",
+       "      <td>0.107971</td>\n",
+       "      <td>0.020257</td>\n",
+       "      <td>0.216171</td>\n",
+       "      <td>0.572421</td>\n",
+       "      <td>0.147785</td>\n",
+       "      <td>0.106555</td>\n",
+       "      <td>0.096098</td>\n",
+       "      <td>0.279161</td>\n",
+       "      <td>0.162524</td>\n",
+       "      <td>0.332554</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
@@ -4028,16 +4494,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000218</td>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.000263</td>\n",
-       "      <td>0.000122</td>\n",
-       "      <td>0.000352</td>\n",
-       "      <td>0.000184</td>\n",
-       "      <td>0.000139</td>\n",
-       "      <td>0.000887</td>\n",
-       "      <td>0.000173</td>\n",
-       "      <td>0.000558</td>\n",
+       "      <td>-0.256655</td>\n",
+       "      <td>-0.163457</td>\n",
+       "      <td>-0.266899</td>\n",
+       "      <td>-0.247330</td>\n",
+       "      <td>-0.232560</td>\n",
+       "      <td>-0.386224</td>\n",
+       "      <td>-0.155104</td>\n",
+       "      <td>-0.059262</td>\n",
+       "      <td>-0.081452</td>\n",
+       "      <td>-0.156595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
@@ -4052,16 +4518,16 @@
        "      <td>163840</td>\n",
        "      <td>174642</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.000386</td>\n",
-       "      <td>-0.000480</td>\n",
-       "      <td>-0.000415</td>\n",
-       "      <td>-0.000092</td>\n",
-       "      <td>-0.000513</td>\n",
-       "      <td>-0.000241</td>\n",
-       "      <td>-0.000285</td>\n",
-       "      <td>-0.000141</td>\n",
-       "      <td>-0.000277</td>\n",
-       "      <td>-0.000758</td>\n",
+       "      <td>0.100300</td>\n",
+       "      <td>0.018010</td>\n",
+       "      <td>0.212837</td>\n",
+       "      <td>0.573748</td>\n",
+       "      <td>0.151573</td>\n",
+       "      <td>0.110227</td>\n",
+       "      <td>0.088412</td>\n",
+       "      <td>0.286110</td>\n",
+       "      <td>0.165036</td>\n",
+       "      <td>0.329597</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4120,109 +4586,109 @@
        "47  chr1  109728807  TTT   G        ALX3  109710224  110234512      -   \n",
        "\n",
        "    gene_mask_start  gene_mask_end  ...  agg_9528  agg_9529  agg_9530  \\\n",
-       "0            163840         172672  ... -0.000096 -0.000190 -0.000135   \n",
-       "1            163840         178946  ... -0.000587 -0.000894 -0.000563   \n",
-       "2            163840         170729  ... -0.000845 -0.001181 -0.000784   \n",
-       "3            163840         165050  ... -0.001767 -0.002189 -0.001705   \n",
-       "4            163840         166306  ... -0.000098  0.000052 -0.000040   \n",
-       "5            163840         167179  ... -0.000603 -0.000860 -0.000584   \n",
-       "6            163840         198383  ... -0.000358 -0.000528 -0.000338   \n",
-       "7            163840         184493  ...  0.001338  0.000849  0.000867   \n",
-       "8            163840         168975  ... -0.000518 -0.000364 -0.000522   \n",
-       "9            163840         173223  ...  0.000109  0.000011  0.000054   \n",
-       "10           163840         170339  ... -0.000278 -0.000460 -0.000305   \n",
-       "11           163840         177242  ...  0.007892  0.005266  0.008324   \n",
-       "12           163840         166924  ... -0.000308 -0.000433 -0.000336   \n",
-       "13           163840         166653  ... -0.000255 -0.000385 -0.000261   \n",
-       "14           163840         199838  ... -0.000901 -0.001054 -0.000771   \n",
-       "15           163840         178999  ... -0.000146 -0.000230 -0.000180   \n",
-       "16           163840         168116  ... -0.000458 -0.000697 -0.000491   \n",
-       "17           163840         183816  ... -0.000815 -0.001141 -0.000897   \n",
-       "18           163840         181059  ... -0.000748 -0.001163 -0.000843   \n",
-       "19           163840         176946  ...  0.000021  0.000008  0.000037   \n",
-       "20           163840         177982  ... -0.000325 -0.000491 -0.000359   \n",
-       "21           163840         172928  ... -0.000077 -0.000098 -0.000090   \n",
-       "22           163840         180515  ...  0.000103  0.000070  0.000060   \n",
-       "23           163840         180515  ...  0.003317  0.005164  0.002698   \n",
-       "24           163840         179428  ...  0.002225  0.001688  0.001366   \n",
-       "25           163840         179428  ... -0.004184 -0.003700 -0.003180   \n",
-       "26           163840         173165  ... -0.000055 -0.000036  0.000088   \n",
-       "27           163840         173165  ...  0.000644 -0.000063  0.001706   \n",
-       "28           163840         172720  ...  0.000533  0.000539  0.000585   \n",
-       "29           163840         172720  ...  0.003902  0.002963  0.001609   \n",
-       "30           163840         172374  ...  0.000144  0.000244  0.000101   \n",
-       "31           163840         172374  ...  0.000287  0.000869  0.000338   \n",
-       "32           163840         170946  ... -0.000685 -0.000671 -0.000490   \n",
-       "33           163840         170946  ...  0.014859  0.029183  0.015364   \n",
-       "34           163840         233549  ...  0.002294  0.002954  0.003762   \n",
-       "35           163840         233549  ... -0.002409 -0.002181  0.001515   \n",
-       "36           163840         179789  ...  0.000565  0.001265  0.001679   \n",
-       "37           163840         179789  ...  0.015521  0.005221  0.011660   \n",
-       "38           163840         182577  ... -0.000140  0.001370  0.000755   \n",
-       "39           163840         182577  ... -0.032480 -0.033616 -0.029502   \n",
-       "40           163840         205369  ... -0.001281 -0.001583 -0.000117   \n",
-       "41           163840         205369  ...  0.006389  0.010391  0.008292   \n",
-       "42           163840         185065  ...  0.001077  0.002305  0.001138   \n",
-       "43           163840         185065  ...  0.009587  0.012155  0.002769   \n",
-       "44           163840         227488  ...  0.001978  0.001395  0.001501   \n",
-       "45           163840         227488  ... -0.004803  0.004060 -0.018828   \n",
-       "46           163840         174642  ...  0.000218  0.000532  0.000263   \n",
-       "47           163840         174642  ... -0.000386 -0.000480 -0.000415   \n",
+       "0            163840         172672  ... -0.038816 -0.132628  0.012945   \n",
+       "1            163840         178946  ...  0.007422 -0.040294  0.037184   \n",
+       "2            163840         170729  ... -0.039081 -0.132857  0.012576   \n",
+       "3            163840         165050  ...  0.007090 -0.040752  0.036865   \n",
+       "4            163840         166306  ...  0.384038  0.383892  0.209059   \n",
+       "5            163840         167179  ...  0.128468  0.201122  0.096130   \n",
+       "6            163840         198383  ...  0.382609  0.387782  0.206949   \n",
+       "7            163840         184493  ...  0.127029  0.199535  0.092907   \n",
+       "8            163840         168975  ... -0.065035 -0.009808 -0.089304   \n",
+       "9            163840         173223  ...  0.149715  0.036652  0.236477   \n",
+       "10           163840         170339  ... -0.064055 -0.008839 -0.088600   \n",
+       "11           163840         177242  ...  0.148965  0.036048  0.236015   \n",
+       "12           163840         166924  ...  0.025167  0.316047  0.052610   \n",
+       "13           163840         166653  ... -0.060978  0.136151  0.222250   \n",
+       "14           163840         199838  ...  0.024651  0.315260  0.050833   \n",
+       "15           163840         178999  ... -0.069684  0.130147  0.214494   \n",
+       "16           163840         168116  ... -0.068721 -0.131200 -0.095311   \n",
+       "17           163840         183816  ... -0.000722 -0.065477 -0.015448   \n",
+       "18           163840         181059  ... -0.068239 -0.130473 -0.095077   \n",
+       "19           163840         176946  ... -0.000799 -0.065457 -0.015547   \n",
+       "20           163840         177982  ...  0.144724  0.096923  0.053529   \n",
+       "21           163840         172928  ... -0.204588 -0.164875 -0.161451   \n",
+       "22           163840         180515  ...  0.145371  0.099419  0.055633   \n",
+       "23           163840         180515  ... -0.216764 -0.175109 -0.172126   \n",
+       "24           163840         179428  ...  0.125082  0.187445  0.094098   \n",
+       "25           163840         179428  ... -0.072443 -0.081550 -0.038486   \n",
+       "26           163840         173165  ...  0.125229  0.187538  0.094372   \n",
+       "27           163840         173165  ... -0.072734 -0.082032 -0.038806   \n",
+       "28           163840         172720  ... -0.109239 -0.095734 -0.046736   \n",
+       "29           163840         172720  ... -0.014338 -0.047306  0.040593   \n",
+       "30           163840         172374  ... -0.114962 -0.101682 -0.051288   \n",
+       "31           163840         172374  ... -0.014739 -0.051509  0.039989   \n",
+       "32           163840         170946  ...  0.017597 -0.080504  0.049995   \n",
+       "33           163840         170946  ... -0.297318  0.036640  0.039409   \n",
+       "34           163840         233549  ...  0.016951 -0.081113  0.048497   \n",
+       "35           163840         233549  ... -0.296372  0.026870  0.034203   \n",
+       "36           163840         179789  ...  0.172656  0.238587 -0.112662   \n",
+       "37           163840         179789  ... -0.587357 -0.383792 -0.075256   \n",
+       "38           163840         182577  ...  0.196620  0.244671 -0.095485   \n",
+       "39           163840         182577  ... -0.598168 -0.350979 -0.061809   \n",
+       "40           163840         205369  ... -0.065015 -0.134536 -0.140023   \n",
+       "41           163840         205369  ... -0.138112 -0.135524 -0.122887   \n",
+       "42           163840         185065  ... -0.064923 -0.134493 -0.140439   \n",
+       "43           163840         185065  ... -0.138915 -0.137064 -0.123798   \n",
+       "44           163840         227488  ... -0.260050 -0.168407 -0.271245   \n",
+       "45           163840         227488  ...  0.107971  0.020257  0.216171   \n",
+       "46           163840         174642  ... -0.256655 -0.163457 -0.266899   \n",
+       "47           163840         174642  ...  0.100300  0.018010  0.212837   \n",
        "\n",
        "    agg_9531  agg_9532  agg_9533  agg_9535  agg_9536  agg_9537  agg_9538  \n",
-       "0  -0.000043 -0.000076 -0.000072 -0.000106  0.000029 -0.000055 -0.000119  \n",
-       "1  -0.000324 -0.000471 -0.000258 -0.000382 -0.000683 -0.000459 -0.000757  \n",
-       "2  -0.000442 -0.000589 -0.000313 -0.000526 -0.000856 -0.000637 -0.000881  \n",
-       "3  -0.001091 -0.001184 -0.000717 -0.001306 -0.001550 -0.001349 -0.001550  \n",
-       "4  -0.000051  0.000037  0.000028 -0.000024  0.000093 -0.000080 -0.000203  \n",
-       "5  -0.000328 -0.000361 -0.000211 -0.000395 -0.000428 -0.000371 -0.000468  \n",
-       "6  -0.000193 -0.000205 -0.000113 -0.000245 -0.000265 -0.000241 -0.000347  \n",
-       "7   0.000903  0.001198  0.001515  0.001150  0.001019  0.000498  0.001034  \n",
-       "8  -0.000307 -0.000214 -0.000158 -0.000330 -0.000363 -0.000348 -0.000509  \n",
-       "9   0.000009 -0.000056 -0.000199 -0.000207 -0.000112  0.000043  0.000051  \n",
-       "10 -0.000142 -0.000220 -0.000106 -0.000170 -0.000272 -0.000170 -0.000231  \n",
-       "11  0.007764 -0.001580  0.002468  0.008656  0.002925  0.007485  0.010022  \n",
-       "12 -0.000197 -0.000195 -0.000144 -0.000220 -0.000190 -0.000199 -0.000302  \n",
-       "13 -0.000158 -0.000164 -0.000123 -0.000158 -0.000281 -0.000191 -0.000319  \n",
-       "14 -0.001977 -0.000437 -0.000555 -0.001084 -0.000583 -0.001613 -0.000536  \n",
-       "15 -0.000099 -0.000132 -0.000082 -0.000092 -0.000189 -0.000112 -0.000195  \n",
-       "16 -0.000359 -0.000367 -0.000241 -0.000268 -0.000487 -0.000344 -0.000595  \n",
-       "17 -0.000674 -0.000717 -0.000515 -0.000520 -0.000780 -0.000614 -0.001010  \n",
-       "18 -0.000615 -0.000704 -0.000456 -0.000404 -0.000761 -0.000572 -0.000986  \n",
-       "19  0.000035  0.000096  0.000057  0.000009  0.000184  0.000039  0.000044  \n",
-       "20 -0.000260 -0.000322 -0.000207 -0.000179 -0.000336 -0.000235 -0.000382  \n",
-       "21 -0.000067 -0.000092 -0.000057 -0.000034 -0.000212 -0.000063 -0.000159  \n",
-       "22  0.000038  0.000027  0.000055  0.000078  0.000277  0.000062  0.000245  \n",
-       "23  0.001438  0.001558  0.001273  0.001937  0.005484  0.002190  0.005321  \n",
-       "24  0.001251  0.001342  0.000177  0.001402  0.000480  0.001663  0.001764  \n",
-       "25 -0.004169 -0.003774 -0.003399 -0.003382  0.000949 -0.002237 -0.002981  \n",
-       "26 -0.000085  0.000037 -0.000040 -0.000056 -0.000044 -0.000046 -0.000027  \n",
-       "27  0.001486  0.003162  0.001671  0.000299  0.002102  0.001007  0.001831  \n",
-       "28  0.000474  0.000076  0.000280  0.000422 -0.000033  0.000436  0.000369  \n",
-       "29  0.002841  0.000024  0.000726  0.001582  0.000497  0.002237 -0.000092  \n",
-       "30  0.000130 -0.000119 -0.000066  0.000112  0.000059  0.000113  0.000135  \n",
-       "31  0.000516  0.000702  0.000260  0.000547  0.000815  0.000190  0.000561  \n",
-       "32 -0.000305 -0.000203 -0.000045 -0.000183 -0.000423 -0.000412 -0.000544  \n",
-       "33  0.007996  0.015255  0.007950  0.010278  0.021851  0.009230  0.024402  \n",
-       "34  0.001515 -0.001879 -0.003638  0.004167 -0.000977  0.003568  0.001709  \n",
-       "35  0.007504  0.011276  0.019385 -0.002945  0.020405 -0.001569  0.007003  \n",
-       "36 -0.000034 -0.002686 -0.000878  0.001329 -0.001112  0.002059  0.000288  \n",
-       "37  0.028807  0.010368  0.014050  0.007810  0.026553  0.009914  0.026500  \n",
-       "38  0.000528 -0.000446 -0.000184 -0.001062  0.000593  0.000575  0.002940  \n",
-       "39 -0.050562 -0.030190 -0.021981 -0.012192 -0.029633 -0.037296 -0.067220  \n",
-       "40 -0.000501 -0.001837 -0.001011 -0.002222  0.000031  0.000068  0.000062  \n",
-       "41  0.010647 -0.005511  0.002638 -0.004853 -0.009829  0.008542  0.011590  \n",
-       "42 -0.000056 -0.000738  0.000059  0.000456 -0.000396  0.000904  0.000512  \n",
-       "43  0.018908  0.010602  0.000302  0.003231  0.007630  0.011715  0.019595  \n",
-       "44  0.000012 -0.000538 -0.000914  0.002817 -0.001998  0.001149  0.002069  \n",
-       "45 -0.000383  0.042374  0.022364 -0.023450  0.043353  0.006033 -0.006614  \n",
-       "46  0.000122  0.000352  0.000184  0.000139  0.000887  0.000173  0.000558  \n",
-       "47 -0.000092 -0.000513 -0.000241 -0.000285 -0.000141 -0.000277 -0.000758  \n",
+       "0  -0.057034 -0.045095 -0.019845 -0.048861 -0.248287 -0.157361 -0.202191  \n",
+       "1  -0.039102 -0.069273 -0.089758  0.085668 -0.100051  0.199948 -0.228135  \n",
+       "2  -0.057286 -0.044211 -0.019335 -0.049245 -0.247860 -0.158028 -0.202599  \n",
+       "3  -0.038709 -0.069798 -0.090022  0.085745 -0.100093  0.199475 -0.228633  \n",
+       "4   0.197611  0.147189  0.214235  0.429188  0.046692  0.232973  0.122444  \n",
+       "5   0.117577 -0.126177 -0.059776  0.184826 -0.009697  0.305417 -0.107728  \n",
+       "6   0.192670  0.148521  0.211755  0.428720  0.039945  0.232015  0.117866  \n",
+       "7   0.114106 -0.132683 -0.060256  0.180594 -0.008448  0.303062 -0.112591  \n",
+       "8  -0.123990 -0.237687 -0.145043 -0.027265 -0.172405 -0.209000 -0.169588  \n",
+       "9  -0.033082 -0.059123 -0.109544 -0.116550 -0.056748  0.238428 -0.066090  \n",
+       "10 -0.123287 -0.236878 -0.144462 -0.026132 -0.171725 -0.208052 -0.169746  \n",
+       "11 -0.033107 -0.059445 -0.110096 -0.117363 -0.057286  0.237902 -0.067406  \n",
+       "12 -0.128209 -0.025701  0.018469  0.071030  0.035337  0.035106  0.198629  \n",
+       "13  0.180415 -0.077709  0.059230 -0.152584  0.021212  0.387905  0.087836  \n",
+       "14 -0.121852 -0.024862  0.020706  0.065433  0.039559  0.038879  0.207624  \n",
+       "15  0.163851 -0.088984  0.049194 -0.161672  0.012433  0.376862  0.079508  \n",
+       "16  0.046062  0.058227 -0.014569 -0.110806  0.156508  0.050760  0.086102  \n",
+       "17 -0.048512  0.080478  0.044108 -0.040190 -0.109651  0.016822 -0.062620  \n",
+       "18  0.046099  0.058977 -0.014226 -0.110129  0.156933  0.051034  0.086115  \n",
+       "19 -0.048479  0.080698  0.044615 -0.040375 -0.109146  0.016773 -0.063049  \n",
+       "20  0.178255  0.186835 -0.086366  0.078235  0.339634  0.164180  0.351917  \n",
+       "21 -0.077614  0.001604 -0.079316 -0.283131 -0.060867 -0.083952 -0.061946  \n",
+       "22  0.177352  0.190575 -0.084158  0.083270  0.344398  0.166967  0.355001  \n",
+       "23 -0.086336 -0.018204 -0.098414 -0.295143 -0.081368 -0.096516 -0.078732  \n",
+       "24  0.073541  0.153524  0.122184  0.178186  0.325020  0.131711  0.251710  \n",
+       "25 -0.047714  0.226176  0.045261 -0.126760  0.371777  0.060598 -0.005164  \n",
+       "26  0.073884  0.154733  0.122949  0.178414  0.326226  0.132067  0.251894  \n",
+       "27 -0.047959  0.226084  0.045132 -0.126927  0.371727  0.060446 -0.005538  \n",
+       "28 -0.160156 -0.108054 -0.116374  0.049736  0.170926  0.112000  0.250690  \n",
+       "29  0.034276  0.086477 -0.002776 -0.036525  0.105931  0.075943 -0.094462  \n",
+       "30 -0.167562 -0.114795 -0.118255  0.045381  0.164219  0.106147  0.242618  \n",
+       "31  0.034758  0.078923 -0.006194 -0.037574  0.102354  0.074973 -0.093299  \n",
+       "32 -0.011808 -0.271627 -0.061634 -0.068092 -0.169892 -0.139209 -0.134266  \n",
+       "33  0.172793  0.131960  0.099102 -0.127269  0.248293  0.078142  0.244484  \n",
+       "34 -0.012860 -0.272076 -0.061712 -0.068612 -0.168814 -0.140736 -0.135443  \n",
+       "35  0.171992  0.132727  0.100970 -0.133759  0.247039  0.073710  0.242920  \n",
+       "36 -0.397093 -0.048434 -0.349497  0.178258 -0.541721 -0.432872 -0.115762  \n",
+       "37  0.116061 -0.107381 -0.537354 -0.290173  0.274848 -0.387343 -0.475388  \n",
+       "38 -0.382966 -0.053531 -0.346830  0.205384 -0.547652 -0.427548 -0.109037  \n",
+       "39  0.137094 -0.114290 -0.549722 -0.316452  0.281865 -0.367328 -0.457750  \n",
+       "40  0.024319 -0.060507 -0.108153 -0.149791 -0.038775 -0.076944 -0.118178  \n",
+       "41  0.003460 -0.095921 -0.142851 -0.133408 -0.044298 -0.079864  0.063350  \n",
+       "42  0.024307 -0.060060 -0.106959 -0.150438 -0.038277 -0.077278 -0.118272  \n",
+       "43  0.002397 -0.096466 -0.143198 -0.133723 -0.044947 -0.080987  0.062265  \n",
+       "44 -0.247335 -0.234919 -0.383042 -0.163728 -0.055309 -0.088513 -0.155641  \n",
+       "45  0.572421  0.147785  0.106555  0.096098  0.279161  0.162524  0.332554  \n",
+       "46 -0.247330 -0.232560 -0.386224 -0.155104 -0.059262 -0.081452 -0.156595  \n",
+       "47  0.573748  0.151573  0.110227  0.088412  0.286110  0.165036  0.329597  \n",
        "\n",
        "[48 rows x 8870 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4247,19 +4713,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_metadata:latest, 628.05MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1290.4MB/s)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from decima.data.dataset import VariantDataset\n",
     "\n",
@@ -4275,7 +4731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -4284,7 +4740,7 @@
        "164"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4295,7 +4751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -4309,7 +4765,7 @@
        " 'warning': []}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4320,7 +4776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -4329,7 +4785,7 @@
        "torch.Size([5, 524288])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4340,7 +4796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -4495,10 +4951,10 @@
        "      <td>+</td>\n",
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
-       "      <td>180346</td>\n",
+       "      <td>180345</td>\n",
        "      <td>TTT</td>\n",
        "      <td>G</td>\n",
-       "      <td>16506</td>\n",
+       "      <td>16505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>78</th>\n",
@@ -4512,10 +4968,10 @@
        "      <td>+</td>\n",
        "      <td>163840</td>\n",
        "      <td>227488</td>\n",
-       "      <td>180867</td>\n",
+       "      <td>180866</td>\n",
        "      <td>T</td>\n",
        "      <td>GG</td>\n",
-       "      <td>17027</td>\n",
+       "      <td>17026</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
@@ -4594,8 +5050,8 @@
        "3            163840         178946   120841      A      G    -42999  \n",
        "4            163840         170729   145915      C      T    -17925  \n",
        "..              ...            ...      ...    ...    ...       ...  \n",
-       "77           163840         227488   180346    TTT      G     16506  \n",
-       "78           163840         227488   180867      T     GG     17027  \n",
+       "77           163840         227488   180345    TTT      G     16505  \n",
+       "78           163840         227488   180866      T     GG     17026  \n",
        "79           163840         174642   507041      T      G    343201  \n",
        "80           163840         174642   506226    AAA      C    342386  \n",
        "81           163840         174642   505705      A     CC    341865  \n",
@@ -4603,7 +5059,7 @@
        "[82 rows x 14 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4616,27 +5072,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's load model"
+    "Let's load the ensemble model of all 4 Decima replicates:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact decima_rep0:latest, 2155.88MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:1.2 (1808.4MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact human_state_dict_fold0:latest, 709.30MB. 1 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 0:0:0.5 (1487.8MB/s)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from decima.hub import load_decima_model\n",
     "\n",
@@ -4652,31 +5095,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n",
       "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "/home/celikm5/miniforge3/envs/decima/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=95` in the `DataLoader` to improve performance.\n"
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "24aebc5e88744bd7b1361c1fde3bcd26",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
-      ]
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
      },
      "metadata": {},
      "output_type": "display_data"
@@ -4690,12 +5128,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `preds` are predicted expression for `alt - ref` alleles for each variant:"
+    "The `preds` are the difference between `alt - ref` allele predictions for each variant-gene pair, i.e. the predicted log fold change in gene expression."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -4704,7 +5142,7 @@
        "(82, 8856)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4715,28 +5153,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[-3.1028390e-03, -3.5525560e-03, -3.5931766e-03, ...,\n",
-       "        -5.2702725e-03, -3.3913553e-03, -6.6891909e-03],\n",
-       "       [ 1.1469424e-04,  1.7350912e-04,  8.1568956e-05, ...,\n",
-       "         1.3417006e-04,  1.1980534e-05, -2.7149916e-05],\n",
-       "       [ 6.0230494e-05, -8.2150102e-05, -2.6747584e-05, ...,\n",
-       "        -2.7078092e-03, -1.8020868e-03, -3.0442923e-03],\n",
+       "array([[-2.8989162e-02, -3.0541826e-02, -2.7764369e-02, ...,\n",
+       "        -1.3684332e-02,  8.4971692e-03,  1.4721267e-03],\n",
+       "       [-2.7411431e-04, -2.8015859e-04, -1.4923699e-04, ...,\n",
+       "        -4.4744834e-04,  1.4175382e-04, -1.6565435e-04],\n",
+       "       [ 4.0262192e-04, -7.6846220e-05, -2.2572372e-04, ...,\n",
+       "         2.9184297e-04,  8.5824914e-04,  2.2569953e-03],\n",
        "       ...,\n",
-       "       [ 5.8137625e-04,  6.4936280e-04,  4.9999356e-04, ...,\n",
-       "         8.4596872e-04,  1.6248971e-04,  5.3709745e-04],\n",
-       "       [ 2.6370510e-03,  2.7481169e-03,  1.8703863e-03, ...,\n",
-       "         5.3109229e-04, -1.4962628e-04, -3.7675351e-04],\n",
-       "       [-2.8469041e-03, -3.2258853e-03, -2.4763122e-03, ...,\n",
-       "        -3.5528541e-03, -8.5747242e-04, -2.2805035e-03]], dtype=float32)"
+       "       [-2.1296535e-02, -2.1238890e-02, -2.0570286e-02, ...,\n",
+       "        -1.9429259e-02, -2.6463622e-02, -2.1707583e-02],\n",
+       "       [ 2.5475025e-04,  5.4145232e-04,  3.5906211e-04, ...,\n",
+       "         3.4752116e-04,  5.1470287e-04,  5.6060962e-04],\n",
+       "       [ 2.3415126e-03,  1.7900020e-03,  2.0225085e-03, ...,\n",
+       "        -1.5570819e-03,  2.8256699e-03, -2.7912110e-04]], dtype=float32)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4747,16 +5185,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Counter({'allele_mismatch_with_reference_genome': 26, 'unknown': 0})"
+       "Counter({'allele_mismatch_with_reference_genome': tensor(13),\n",
+       "         'unknown': tensor(0)})"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4774,7 +5213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4784,7 +5223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -4793,7 +5232,7 @@
        "torch.Size([2, 5, 524288])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4804,7 +5243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4838,7 +5277,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -4855,13 +5294,27 @@
     "plt.xlabel(\"gene expression for ref allele\")\n",
     "plt.ylabel(\"gene expression for alt allele\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python (decima)",
    "language": "python",
-   "name": "python3"
+   "name": "decima"
   },
   "language_info": {
    "codemirror_mode": {
@@ -4873,7 +5326,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/3-finetune.ipynb b/docs/tutorials/3-finetune.ipynb
index 19a049e..6ce55f9 100644
--- a/docs/tutorials/3-finetune.ipynb
+++ b/docs/tutorials/3-finetune.ipynb
@@ -12,14 +12,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "c6dbf5fc-85ca-42a8-b076-ba0313604e91",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:18.270014Z",
-     "iopub.status.busy": "2025-11-21T22:29:18.269887Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.533418Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.532666Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import glob\n",
@@ -34,14 +27,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "a8563bf1-0305-437b-81fa-0584753c5793",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.535200Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.534846Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.537436Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.537029Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "inputdir = \"./data\"\n",
@@ -70,19 +56,12 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "83273dca-0622-42e2-a606-b645e0a31f19",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.538717Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.538588Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.580706Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.580293Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AnnData object with n_obs × n_vars = 50 × 921\n",
+       "AnnData object with n_obs × n_vars = 50 × 920\n",
        "    obs: 'cell_type', 'tissue', 'disease', 'study'\n",
        "    var: 'chrom', 'start', 'end', 'strand', 'gene_start', 'gene_end', 'gene_length', 'gene_mask_start', 'gene_mask_end', 'dataset'\n",
        "    uns: 'log1p'"
@@ -112,14 +91,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "e29ca0c0-5f61-4146-b187-d11cc57373d0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.581807Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.581680Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.602279Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.601917Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -220,14 +192,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "a79a70c0-5a33-46dc-b363-4e9df6ab2b8a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.603573Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.603444Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.609192Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.608798Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -266,11 +231,11 @@
        "    <tr>\n",
        "      <th>gene_0</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>26191000</td>\n",
+       "      <td>26715288</td>\n",
+       "      <td>+</td>\n",
        "      <td>26354840</td>\n",
        "      <td>26879128</td>\n",
-       "      <td>+</td>\n",
-       "      <td>26518680</td>\n",
-       "      <td>27042968</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -279,11 +244,11 @@
        "    <tr>\n",
        "      <th>gene_1</th>\n",
        "      <td>chr19</td>\n",
+       "      <td>41275257</td>\n",
+       "      <td>41799545</td>\n",
+       "      <td>-</td>\n",
        "      <td>41111417</td>\n",
        "      <td>41635705</td>\n",
-       "      <td>-</td>\n",
-       "      <td>40947577</td>\n",
-       "      <td>41471865</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -292,11 +257,11 @@
        "    <tr>\n",
        "      <th>gene_2</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>79937866</td>\n",
+       "      <td>80462154</td>\n",
+       "      <td>-</td>\n",
        "      <td>79774026</td>\n",
        "      <td>80298314</td>\n",
-       "      <td>-</td>\n",
-       "      <td>79610186</td>\n",
-       "      <td>80134474</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -305,11 +270,11 @@
        "    <tr>\n",
        "      <th>gene_4</th>\n",
        "      <td>chr16</td>\n",
+       "      <td>3905208</td>\n",
+       "      <td>4429496</td>\n",
+       "      <td>-</td>\n",
        "      <td>3741368</td>\n",
        "      <td>4265656</td>\n",
-       "      <td>-</td>\n",
-       "      <td>3577528</td>\n",
-       "      <td>4101816</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -318,11 +283,11 @@
        "    <tr>\n",
        "      <th>gene_5</th>\n",
        "      <td>chr10</td>\n",
+       "      <td>22495641</td>\n",
+       "      <td>23019929</td>\n",
+       "      <td>+</td>\n",
        "      <td>22659481</td>\n",
        "      <td>23183769</td>\n",
-       "      <td>+</td>\n",
-       "      <td>22823321</td>\n",
-       "      <td>23347609</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -334,11 +299,11 @@
       ],
       "text/plain": [
        "        chrom     start       end strand  gene_start  gene_end  gene_length  \\\n",
-       "gene_0   chr1  26354840  26879128      +    26518680  27042968       524288   \n",
-       "gene_1  chr19  41111417  41635705      -    40947577  41471865       524288   \n",
-       "gene_2   chr1  79774026  80298314      -    79610186  80134474       524288   \n",
-       "gene_4  chr16   3741368   4265656      -     3577528   4101816       524288   \n",
-       "gene_5  chr10  22659481  23183769      +    22823321  23347609       524288   \n",
+       "gene_0   chr1  26191000  26715288      +    26354840  26879128       524288   \n",
+       "gene_1  chr19  41275257  41799545      -    41111417  41635705       524288   \n",
+       "gene_2   chr1  79937866  80462154      -    79774026  80298314       524288   \n",
+       "gene_4  chr16   3905208   4429496      -     3741368   4265656       524288   \n",
+       "gene_5  chr10  22495641  23019929      +    22659481  23183769       524288   \n",
        "\n",
        "        gene_mask_start  gene_mask_end dataset  \n",
        "gene_0           163840         524288   train  \n",
@@ -369,23 +334,16 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "3fd9bd2a-e728-4dc5-9c90-a9558fab0e27",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.610372Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.610248Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.613043Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.612649Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.       , 7.2926292, 7.2926292, 7.2926292, 7.2926292],\n",
-       "       [7.3133874, 7.3133874, 0.       , 7.3133874, 7.3133874],\n",
-       "       [7.299993 , 7.299993 , 7.299993 , 7.299993 , 0.       ],\n",
-       "       [7.299993 , 0.       , 7.299993 , 7.299993 , 0.       ],\n",
-       "       [7.3376517, 7.3376517, 0.       , 7.3376517, 7.3376517]],\n",
+       "array([[0.       , 7.295568 , 7.295568 , 7.295568 , 7.295568 ],\n",
+       "       [7.316388 , 7.316388 , 0.       , 7.316388 , 7.316388 ],\n",
+       "       [7.3014727, 7.3014727, 7.3014727, 7.3014727, 0.       ],\n",
+       "       [7.3014727, 0.       , 7.3014727, 7.3014727, 0.       ],\n",
+       "       [7.3407264, 7.3407264, 0.       , 7.3407264, 7.3407264]],\n",
        "      dtype=float32)"
       ]
      },
@@ -418,14 +376,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "34115f7a-aaf8-4ca3-abbb-a4fc552bf5a7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.614156Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.614039Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.616992Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.616589Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -444,23 +395,16 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "e42a91c7-ac01-45b3-8d3b-6c99baf7adff",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.618117Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.617994Z",
-     "iopub.status.idle": "2025-11-21T22:29:26.620685Z",
-     "shell.execute_reply": "2025-11-21T22:29:26.620286Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.       , 7.295568 , 7.295568 , 7.295568 , 7.295568 ],\n",
-       "       [7.316388 , 7.316388 , 0.       , 7.316388 , 7.316388 ],\n",
-       "       [7.3014727, 7.3014727, 7.3014727, 7.3014727, 0.       ],\n",
+       "array([[0.       , 7.297041 , 7.297041 , 7.297041 , 7.297041 ],\n",
+       "       [7.317892 , 7.317892 , 0.       , 7.317892 , 7.317892 ],\n",
+       "       [7.302954 , 7.302954 , 7.302954 , 7.302954 , 0.       ],\n",
        "       [7.3014727, 0.       , 7.3014727, 7.3014727, 0.       ],\n",
-       "       [7.3407264, 7.3407264, 0.       , 7.3407264, 7.3407264]],\n",
+       "       [7.3422675, 7.3422675, 0.       , 7.3422675, 7.3422675]],\n",
        "      dtype=float32)"
       ]
      },
@@ -493,26 +437,8 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "86905140-4b30-424b-91ce-090a0a56ebab",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:26.621914Z",
-     "iopub.status.busy": "2025-11-21T22:29:26.621802Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.486339Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.485817Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
-      "  warnings.warn(\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from decima.data.preprocess import var_to_intervals"
    ]
@@ -521,14 +447,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "d027eb23-48d6-40d9-9e30-b5c7691a7c53",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.487901Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.487652Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.494114Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.493712Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -567,11 +486,11 @@
        "    <tr>\n",
        "      <th>gene_0</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>26191000</td>\n",
+       "      <td>26715288</td>\n",
+       "      <td>+</td>\n",
        "      <td>26354840</td>\n",
        "      <td>26879128</td>\n",
-       "      <td>+</td>\n",
-       "      <td>26518680</td>\n",
-       "      <td>27042968</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -580,11 +499,11 @@
        "    <tr>\n",
        "      <th>gene_1</th>\n",
        "      <td>chr19</td>\n",
+       "      <td>41275257</td>\n",
+       "      <td>41799545</td>\n",
+       "      <td>-</td>\n",
        "      <td>41111417</td>\n",
        "      <td>41635705</td>\n",
-       "      <td>-</td>\n",
-       "      <td>40947577</td>\n",
-       "      <td>41471865</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -593,11 +512,11 @@
        "    <tr>\n",
        "      <th>gene_2</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>79937866</td>\n",
+       "      <td>80462154</td>\n",
+       "      <td>-</td>\n",
        "      <td>79774026</td>\n",
        "      <td>80298314</td>\n",
-       "      <td>-</td>\n",
-       "      <td>79610186</td>\n",
-       "      <td>80134474</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -606,11 +525,11 @@
        "    <tr>\n",
        "      <th>gene_4</th>\n",
        "      <td>chr16</td>\n",
+       "      <td>3905208</td>\n",
+       "      <td>4429496</td>\n",
+       "      <td>-</td>\n",
        "      <td>3741368</td>\n",
        "      <td>4265656</td>\n",
-       "      <td>-</td>\n",
-       "      <td>3577528</td>\n",
-       "      <td>4101816</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -619,11 +538,11 @@
        "    <tr>\n",
        "      <th>gene_5</th>\n",
        "      <td>chr10</td>\n",
+       "      <td>22495641</td>\n",
+       "      <td>23019929</td>\n",
+       "      <td>+</td>\n",
        "      <td>22659481</td>\n",
        "      <td>23183769</td>\n",
-       "      <td>+</td>\n",
-       "      <td>22823321</td>\n",
-       "      <td>23347609</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -635,11 +554,11 @@
       ],
       "text/plain": [
        "        chrom     start       end strand  gene_start  gene_end  gene_length  \\\n",
-       "gene_0   chr1  26354840  26879128      +    26518680  27042968       524288   \n",
-       "gene_1  chr19  41111417  41635705      -    40947577  41471865       524288   \n",
-       "gene_2   chr1  79774026  80298314      -    79610186  80134474       524288   \n",
-       "gene_4  chr16   3741368   4265656      -     3577528   4101816       524288   \n",
-       "gene_5  chr10  22659481  23183769      +    22823321  23347609       524288   \n",
+       "gene_0   chr1  26191000  26715288      +    26354840  26879128       524288   \n",
+       "gene_1  chr19  41275257  41799545      -    41111417  41635705       524288   \n",
+       "gene_2   chr1  79937866  80462154      -    79774026  80298314       524288   \n",
+       "gene_4  chr16   3905208   4429496      -     3741368   4265656       524288   \n",
+       "gene_5  chr10  22495641  23019929      +    22659481  23183769       524288   \n",
        "\n",
        "        gene_mask_start  gene_mask_end dataset  \n",
        "gene_0           163840         524288   train  \n",
@@ -670,14 +589,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "566977ab-041f-4a3d-b10e-9b6fa717c98e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.495318Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.495198Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.498415Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.498008Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ad.var[\"gene_start\"] = ad.var.start.tolist()\n",
@@ -689,14 +601,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "e23e95dd-6616-4f79-8d1b-3a0fe22816d0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.499458Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.499339Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.504838Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.504440Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -735,11 +640,11 @@
        "    <tr>\n",
        "      <th>gene_0</th>\n",
        "      <td>chr1</td>\n",
-       "      <td>26354840</td>\n",
-       "      <td>26879128</td>\n",
+       "      <td>26191000</td>\n",
+       "      <td>26715288</td>\n",
        "      <td>+</td>\n",
-       "      <td>26354840</td>\n",
-       "      <td>26879128</td>\n",
+       "      <td>26191000</td>\n",
+       "      <td>26715288</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -748,11 +653,11 @@
        "    <tr>\n",
        "      <th>gene_1</th>\n",
        "      <td>chr19</td>\n",
-       "      <td>41111417</td>\n",
-       "      <td>41635705</td>\n",
+       "      <td>41275257</td>\n",
+       "      <td>41799545</td>\n",
        "      <td>-</td>\n",
-       "      <td>41111417</td>\n",
-       "      <td>41635705</td>\n",
+       "      <td>41275257</td>\n",
+       "      <td>41799545</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -761,11 +666,11 @@
        "    <tr>\n",
        "      <th>gene_2</th>\n",
        "      <td>chr1</td>\n",
-       "      <td>79774026</td>\n",
-       "      <td>80298314</td>\n",
+       "      <td>79937866</td>\n",
+       "      <td>80462154</td>\n",
        "      <td>-</td>\n",
-       "      <td>79774026</td>\n",
-       "      <td>80298314</td>\n",
+       "      <td>79937866</td>\n",
+       "      <td>80462154</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -774,11 +679,11 @@
        "    <tr>\n",
        "      <th>gene_4</th>\n",
        "      <td>chr16</td>\n",
-       "      <td>3741368</td>\n",
-       "      <td>4265656</td>\n",
+       "      <td>3905208</td>\n",
+       "      <td>4429496</td>\n",
        "      <td>-</td>\n",
-       "      <td>3741368</td>\n",
-       "      <td>4265656</td>\n",
+       "      <td>3905208</td>\n",
+       "      <td>4429496</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -787,11 +692,11 @@
        "    <tr>\n",
        "      <th>gene_5</th>\n",
        "      <td>chr10</td>\n",
-       "      <td>22659481</td>\n",
-       "      <td>23183769</td>\n",
+       "      <td>22495641</td>\n",
+       "      <td>23019929</td>\n",
        "      <td>+</td>\n",
-       "      <td>22659481</td>\n",
-       "      <td>23183769</td>\n",
+       "      <td>22495641</td>\n",
+       "      <td>23019929</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -803,11 +708,11 @@
       ],
       "text/plain": [
        "        chrom     start       end strand  gene_start  gene_end  gene_length  \\\n",
-       "gene_0   chr1  26354840  26879128      +    26354840  26879128       524288   \n",
-       "gene_1  chr19  41111417  41635705      -    41111417  41635705       524288   \n",
-       "gene_2   chr1  79774026  80298314      -    79774026  80298314       524288   \n",
-       "gene_4  chr16   3741368   4265656      -     3741368   4265656       524288   \n",
-       "gene_5  chr10  22659481  23183769      +    22659481  23183769       524288   \n",
+       "gene_0   chr1  26191000  26715288      +    26191000  26715288       524288   \n",
+       "gene_1  chr19  41275257  41799545      -    41275257  41799545       524288   \n",
+       "gene_2   chr1  79937866  80462154      -    79937866  80462154       524288   \n",
+       "gene_4  chr16   3905208   4429496      -     3905208   4429496       524288   \n",
+       "gene_5  chr10  22495641  23019929      +    22495641  23019929       524288   \n",
        "\n",
        "        gene_mask_start  gene_mask_end dataset  \n",
        "gene_0           163840         524288   train  \n",
@@ -838,14 +743,7 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "c40ebdb5-2d8c-4cce-9685-61d5db0123f3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.505916Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.505798Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.664023Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.663629Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -867,14 +765,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "191ed1e0-d34f-4aa4-a8e7-bc5919642528",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.665110Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.664988Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.670440Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.670046Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -913,11 +804,11 @@
        "    <tr>\n",
        "      <th>gene_0</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>26027160</td>\n",
+       "      <td>26551448</td>\n",
+       "      <td>+</td>\n",
        "      <td>26191000</td>\n",
        "      <td>26715288</td>\n",
-       "      <td>+</td>\n",
-       "      <td>26354840</td>\n",
-       "      <td>26879128</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -926,11 +817,11 @@
        "    <tr>\n",
        "      <th>gene_1</th>\n",
        "      <td>chr19</td>\n",
+       "      <td>41439097</td>\n",
+       "      <td>41963385</td>\n",
+       "      <td>-</td>\n",
        "      <td>41275257</td>\n",
        "      <td>41799545</td>\n",
-       "      <td>-</td>\n",
-       "      <td>41111417</td>\n",
-       "      <td>41635705</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -939,11 +830,11 @@
        "    <tr>\n",
        "      <th>gene_2</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>80101706</td>\n",
+       "      <td>80625994</td>\n",
+       "      <td>-</td>\n",
        "      <td>79937866</td>\n",
        "      <td>80462154</td>\n",
-       "      <td>-</td>\n",
-       "      <td>79774026</td>\n",
-       "      <td>80298314</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -952,11 +843,11 @@
        "    <tr>\n",
        "      <th>gene_4</th>\n",
        "      <td>chr16</td>\n",
+       "      <td>4069048</td>\n",
+       "      <td>4593336</td>\n",
+       "      <td>-</td>\n",
        "      <td>3905208</td>\n",
        "      <td>4429496</td>\n",
-       "      <td>-</td>\n",
-       "      <td>3741368</td>\n",
-       "      <td>4265656</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -965,11 +856,11 @@
        "    <tr>\n",
        "      <th>gene_5</th>\n",
        "      <td>chr10</td>\n",
+       "      <td>22331801</td>\n",
+       "      <td>22856089</td>\n",
+       "      <td>+</td>\n",
        "      <td>22495641</td>\n",
        "      <td>23019929</td>\n",
-       "      <td>+</td>\n",
-       "      <td>22659481</td>\n",
-       "      <td>23183769</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -981,11 +872,11 @@
       ],
       "text/plain": [
        "        chrom     start       end strand  gene_start  gene_end  gene_length  \\\n",
-       "gene_0   chr1  26191000  26715288      +    26354840  26879128       524288   \n",
-       "gene_1  chr19  41275257  41799545      -    41111417  41635705       524288   \n",
-       "gene_2   chr1  79937866  80462154      -    79774026  80298314       524288   \n",
-       "gene_4  chr16   3905208   4429496      -     3741368   4265656       524288   \n",
-       "gene_5  chr10  22495641  23019929      +    22659481  23183769       524288   \n",
+       "gene_0   chr1  26027160  26551448      +    26191000  26715288       524288   \n",
+       "gene_1  chr19  41439097  41963385      -    41275257  41799545       524288   \n",
+       "gene_2   chr1  80101706  80625994      -    79937866  80462154       524288   \n",
+       "gene_4  chr16   4069048   4593336      -     3905208   4429496       524288   \n",
+       "gene_5  chr10  22331801  22856089      +    22495641  23019929       524288   \n",
        "\n",
        "        gene_mask_start  gene_mask_end dataset  \n",
        "gene_0           163840         524288   train  \n",
@@ -1032,14 +923,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "8f4db77e-71f1-4457-b5cf-8c5f8f98ad06",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.671608Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.671488Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.876755Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.876312Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1141,14 +1025,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "99d1a382-1384-41ea-b59a-bb4aaa26caba",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.877970Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.877840Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.907310Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.906870Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1192,7 +1069,7 @@
        "      <td>fold0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>44</th>\n",
+       "      <th>45</th>\n",
        "      <td>gene_4</td>\n",
        "      <td>fold2</td>\n",
        "    </tr>\n",
@@ -1210,7 +1087,7 @@
        "0   gene_0  fold5\n",
        "15  gene_1  fold0\n",
        "30  gene_2  fold0\n",
-       "44  gene_4  fold2\n",
+       "45  gene_4  fold2\n",
        "59  gene_5  fold2"
       ]
      },
@@ -1237,14 +1114,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "14180e40-c296-4f6f-b353-a07f383a7aae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.908528Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.908396Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.911444Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.910959Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "test_genes = overlaps.gene[overlaps.fold_ == \"fold3\"].tolist()\n",
@@ -1264,20 +1134,13 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "ed8411d8-aecf-4196-bcc3-7635c1b4f34a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.912530Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.912405Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.918001Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.917575Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/slurmjob.14477843/ipykernel_3516462/3109841685.py:1: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n"
+      "/tmp/slurmjob.38313776/ipykernel_505470/3109841685.py:1: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n"
      ]
     }
    ],
@@ -1291,14 +1154,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "6e71f3f9-61d5-496d-8b04-661d5e97c2b8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.919025Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.918905Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.924425Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.924026Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1337,11 +1193,11 @@
        "    <tr>\n",
        "      <th>gene_0</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>26027160</td>\n",
+       "      <td>26551448</td>\n",
+       "      <td>+</td>\n",
        "      <td>26191000</td>\n",
        "      <td>26715288</td>\n",
-       "      <td>+</td>\n",
-       "      <td>26354840</td>\n",
-       "      <td>26879128</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -1350,11 +1206,11 @@
        "    <tr>\n",
        "      <th>gene_1</th>\n",
        "      <td>chr19</td>\n",
+       "      <td>41439097</td>\n",
+       "      <td>41963385</td>\n",
+       "      <td>-</td>\n",
        "      <td>41275257</td>\n",
        "      <td>41799545</td>\n",
-       "      <td>-</td>\n",
-       "      <td>41111417</td>\n",
-       "      <td>41635705</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -1363,11 +1219,11 @@
        "    <tr>\n",
        "      <th>gene_2</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>80101706</td>\n",
+       "      <td>80625994</td>\n",
+       "      <td>-</td>\n",
        "      <td>79937866</td>\n",
        "      <td>80462154</td>\n",
-       "      <td>-</td>\n",
-       "      <td>79774026</td>\n",
-       "      <td>80298314</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -1376,11 +1232,11 @@
        "    <tr>\n",
        "      <th>gene_4</th>\n",
        "      <td>chr16</td>\n",
+       "      <td>4069048</td>\n",
+       "      <td>4593336</td>\n",
+       "      <td>-</td>\n",
        "      <td>3905208</td>\n",
        "      <td>4429496</td>\n",
-       "      <td>-</td>\n",
-       "      <td>3741368</td>\n",
-       "      <td>4265656</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -1389,11 +1245,11 @@
        "    <tr>\n",
        "      <th>gene_5</th>\n",
        "      <td>chr10</td>\n",
+       "      <td>22331801</td>\n",
+       "      <td>22856089</td>\n",
+       "      <td>+</td>\n",
        "      <td>22495641</td>\n",
        "      <td>23019929</td>\n",
-       "      <td>+</td>\n",
-       "      <td>22659481</td>\n",
-       "      <td>23183769</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
@@ -1405,11 +1261,11 @@
       ],
       "text/plain": [
        "        chrom     start       end strand  gene_start  gene_end  gene_length  \\\n",
-       "gene_0   chr1  26191000  26715288      +    26354840  26879128       524288   \n",
-       "gene_1  chr19  41275257  41799545      -    41111417  41635705       524288   \n",
-       "gene_2   chr1  79937866  80462154      -    79774026  80298314       524288   \n",
-       "gene_4  chr16   3905208   4429496      -     3741368   4265656       524288   \n",
-       "gene_5  chr10  22495641  23019929      +    22659481  23183769       524288   \n",
+       "gene_0   chr1  26027160  26551448      +    26191000  26715288       524288   \n",
+       "gene_1  chr19  41439097  41963385      -    41275257  41799545       524288   \n",
+       "gene_2   chr1  80101706  80625994      -    79937866  80462154       524288   \n",
+       "gene_4  chr16   4069048   4593336      -     3905208   4429496       524288   \n",
+       "gene_5  chr10  22331801  22856089      +    22495641  23019929       524288   \n",
        "\n",
        "        gene_mask_start  gene_mask_end dataset  \n",
        "gene_0           163840         524288   train  \n",
@@ -1432,22 +1288,15 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "894e5f6f-1d0b-4c71-8f11-fcea38bea97d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.925651Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.925524Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.928822Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.928340Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "dataset\n",
-       "train    766\n",
-       "test      83\n",
-       "val       71\n",
+       "train    765\n",
+       "test      80\n",
+       "val       74\n",
        "Name: count, dtype: int64"
       ]
      },
@@ -1488,14 +1337,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "e55d7251-5372-4744-a1f3-411223d4eb35",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.929937Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.929820Z",
-     "iopub.status.idle": "2025-11-21T22:29:40.998651Z",
-     "shell.execute_reply": "2025-11-21T22:29:40.998240Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ad.write_h5ad(ad_file_path)"
@@ -1521,14 +1363,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "3b1473fd-fd72-41e7-a673-92fb474440ec",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:40.999863Z",
-     "iopub.status.busy": "2025-11-21T22:29:40.999743Z",
-     "iopub.status.idle": "2025-11-21T22:29:41.024192Z",
-     "shell.execute_reply": "2025-11-21T22:29:41.023764Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from decima.data.write_hdf5 import write_hdf5"
@@ -1538,14 +1373,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "925184a3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:41.025478Z",
-     "iopub.status.busy": "2025-11-21T22:29:41.025360Z",
-     "iopub.status.idle": "2025-11-21T22:29:41.191312Z",
-     "shell.execute_reply": "2025-11-21T22:29:41.190672Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "! mkdir -p example"
@@ -1555,14 +1383,7 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "81e3d301-db22-4a00-9cdc-f6ec7c641030",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:29:41.192756Z",
-     "iopub.status.busy": "2025-11-21T22:29:41.192597Z",
-     "iopub.status.idle": "2025-11-21T22:30:41.442605Z",
-     "shell.execute_reply": "2025-11-21T22:30:41.442010Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1570,9 +1391,13 @@
      "text": [
       "Writing metadata\n",
       "Writing task indices\n",
-      "Writing genes array of shape: (920, 2)\n",
-      "Writing labels array of shape: (920, 50, 1)\n",
-      "Making gene masks\n"
+      "Writing genes array of shape: (919, 2)\n",
+      "Writing labels array of shape: (919, 50, 1)\n",
+      "Making gene masks\n",
+      "Writing mask array of shape: (919, 534288)\n",
+      "Encoding sequences\n",
+      "Writing sequence array of shape: (919, 534288)\n",
+      "Done!\n"
      ]
     },
     {
@@ -1615,14 +1440,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "d9ca9ee3-c90a-4848-b9ed-899f21fbf39e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:30:41.444116Z",
-     "iopub.status.busy": "2025-11-21T22:30:41.443952Z",
-     "iopub.status.idle": "2025-11-21T22:30:41.446732Z",
-     "shell.execute_reply": "2025-11-21T22:30:41.446334Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Learning rate default=0.001\n",
@@ -1657,14 +1475,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "676bba10-feaf-4bcf-ae76-91cf163ac26a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:30:41.447823Z",
-     "iopub.status.busy": "2025-11-21T22:30:41.447700Z",
-     "iopub.status.idle": "2025-11-21T22:30:41.450388Z",
-     "shell.execute_reply": "2025-11-21T22:30:41.450000Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cmds = []\n",
@@ -1689,14 +1500,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "6aee97bd-7b06-4af5-834e-c0f08127e75c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:30:41.451403Z",
-     "iopub.status.busy": "2025-11-21T22:30:41.451285Z",
-     "iopub.status.idle": "2025-11-21T22:30:41.453392Z",
-     "shell.execute_reply": "2025-11-21T22:30:41.452977Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1727,22 +1531,6037 @@
    "execution_count": 28,
    "id": "d0fdaa9d",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:30:41.454479Z",
-     "iopub.status.busy": "2025-11-21T22:30:41.454355Z",
-     "iopub.status.idle": "2025-11-21T22:35:16.456721Z",
-     "shell.execute_reply": "2025-11-21T22:35:16.455981Z"
-    }
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
+      "decima - INFO - Data paths: matrix_file=./data/data.h5ad, h5_file=./example/data.h5\n",
+      "decima - INFO - Reading anndata\n",
+      "decima - INFO - Making dataset objects\n",
+      "decima - INFO - train_params: {'batch_size': 1, 'num_workers': 16, 'devices': 0, 'logger': 'wandb', 'save_dir': './example', 'max_epochs': 1, 'lr': 5e-05, 'total_weight': 0.0001, 'accumulate_grad_batches': 5, 'loss': 'poisson_multinomial', 'clip': 0.0, 'save_top_k': 1, 'pin_memory': True}\n",
+      "decima - INFO - model_params: {'n_tasks': 50, 'init_borzoi': True, 'replicate': '0'}\n",
+      "decima - INFO - Initializing model\n",
+      "decima - INFO - Initializing weights from Borzoi model using wandb for replicate: 0\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The anonymous parameter to wandb.login() has no effect and will be removed in future versions.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/lala8/.netrc.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mavantikalal\u001b[0m (\u001b[33mgrelu\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'human_state_dict_fold0:latest', 709.30MB. 1 files...\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
+      "Done. 00:00:27.5 (25.8MB/s)\n",
+      "/home/lala8/decima/src/decima/model/decima_model.py:86: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "decima - INFO - Connecting to wandb.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Updated settings file /home/lala8/.config/wandb/settings\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: [wandb.login()] Loaded credentials for https://genentech.wandb.io from /home/lala8/.netrc.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mlal-avantika\u001b[0m (\u001b[33mgrelu\u001b[0m) to \u001b[32mhttps://genentech.wandb.io\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[38;5;178m⢿\u001b[0m Waiting for wandb.init()...\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[38;5;178m⣻\u001b[0m Waiting for wandb.init()...\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[38;5;178m⣽\u001b[0m Waiting for wandb.init()...\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.26.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1mfinetune_test_0/wandb/run-20260520_155659-g0m7s659\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mfinetune_test_0\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://genentech.wandb.io/grelu/decima\u001b[0m\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://genentech.wandb.io/grelu/decima/runs/g0m7s659\u001b[0m\n",
+      "decima - INFO - Training\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima finetune --name fi ...\n",
+      "Using 16bit Automatic Mixed Precision (AMP)\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/pytorch_lightning/loggers/wandb.py:400: UserWarning: There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse this run. If this is not desired, call `wandb.finish()` before instantiating `WandbLogger`.\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\u001b[2KMultinomial: 17.870079040527344, Poisson: -0.08475557714700699\n",
+      "\u001b[2KMultinomial: 17.161930084228516, Poisson: -0.08194837719202042[2m0:00:01 • -:--:--\u001b[0m \u001b[2;4m0.00it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 23.621156692504883, Poisson: -0.1136205643415451[0m 2/74 \u001b[2m0:00:01 • 0:00:09\u001b[0m \u001b[2;4m8.29it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 18.289478302001953, Poisson: -0.08796687424182892━━━━━━━\u001b[0m 3/74 \u001b[2m0:00:01 • 0:00:09\u001b[0m \u001b[2;4m8.29it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.807384490966797, Poisson: -0.10480411350727081━━━━━━━\u001b[0m 4/74 \u001b[2m0:00:01 • 0:00:09\u001b[0m \u001b[2;4m8.29it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 17.82617950439453, Poisson: -0.08457749336957932━━━━━━━━\u001b[0m 5/74 \u001b[2m0:00:01 • 0:00:09\u001b[0m \u001b[2;4m8.30it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 17.69489288330078, Poisson: -0.0844057947397232━━━━━━━━━\u001b[0m 6/74 \u001b[2m0:00:01 • 0:00:09\u001b[0m \u001b[2;4m8.29it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 22.296825408935547, Poisson: -0.10810919106006622━━━━━━━\u001b[0m 7/74 \u001b[2m0:00:01 • 0:00:09\u001b[0m \u001b[2;4m8.29it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 23.012557983398438, Poisson: -0.1102101281285286━━━━━━━━\u001b[0m 8/74 \u001b[2m0:00:01 • 0:00:08\u001b[0m \u001b[2;4m8.29it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.654335021972656, Poisson: -0.09885435551404953━━━━━━━\u001b[0m 9/74 \u001b[2m0:00:02 • 0:00:08\u001b[0m \u001b[2;4m8.30it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 17.320323944091797, Poisson: -0.08139467984437943━━━━━━\u001b[0m 10/74 \u001b[2m0:00:02 • 0:00:08\u001b[0m \u001b[2;4m8.30it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.90839958190918, Poisson: -0.10467664897441864━━━━━━━\u001b[0m 11/74 \u001b[2m0:00:02 • 0:00:08\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 21.887348175048828, Poisson: -0.10524922609329224━━━━━━\u001b[0m 13/74 \u001b[2m0:00:02 • 0:00:08\u001b[0m \u001b[2;4m8.30it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 24.2436466217041, Poisson: -0.11715485900640488━━━━━━━━\u001b[0m 14/74 \u001b[2m0:00:02 • 0:00:08\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 19.447330474853516, Poisson: -0.09315256774425507━━━━━━\u001b[0m 15/74 \u001b[2m0:00:02 • 0:00:08\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.68655776977539, Poisson: -0.09870058298110962━━━━━━━\u001b[0m 16/74 \u001b[2m0:00:02 • 0:00:07\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.374292373657227, Poisson: -0.10184745490550995━━━━━━\u001b[0m 17/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 16.723011016845703, Poisson: -0.07884380966424942━━━━━━\u001b[0m 18/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 22.9427433013916, Poisson: -0.11061929911375046━━━━━━━━\u001b[0m 19/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.024242401123047, Poisson: -0.09591876715421677━━━━━━\u001b[0m 20/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 24.76435089111328, Poisson: -0.1185229942202568━━━━━━━━\u001b[0m 21/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.826908111572266, Poisson: -0.09878891706466675━━━━━━\u001b[0m 22/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 24.14890480041504, Poisson: -0.11637716740369797━━━━━━━\u001b[0m 23/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 19.612180709838867, Poisson: -0.0931989997625351━━━━━━━\u001b[0m 24/74 \u001b[2m0:00:03 • 0:00:07\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 19.036500930786133, Poisson: -0.09052649885416031━━━━━━\u001b[0m 25/74 \u001b[2m0:00:04 • 0:00:06\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.153146743774414, Poisson: -0.09655505418777466━━━━━━\u001b[0m 26/74 \u001b[2m0:00:04 • 0:00:06\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 17.25844383239746, Poisson: -0.0812654122710228━━━━━━━━\u001b[0m 29/74 \u001b[2m0:00:04 • 0:00:06\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 21.855703353881836, Poisson: -0.10460451245307922━━━━━━\u001b[0m 32/74 \u001b[2m0:00:04 • 0:00:06\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 19.562828063964844, Poisson: -0.09329111874103546━━━━━━\u001b[0m 33/74 \u001b[2m0:00:04 • 0:00:05\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 19.637914657592773, Poisson: -0.09301397204399109━━━━━━\u001b[0m 36/74 \u001b[2m0:00:05 • 0:00:05\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 24.112186431884766, Poisson: -0.11678439378738403━━━━━━\u001b[0m 37/74 \u001b[2m0:00:05 • 0:00:05\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 22.90837287902832, Poisson: -0.11134052276611328━━━━━━━\u001b[0m 39/74 \u001b[2m0:00:05 • 0:00:05\u001b[0m \u001b[2;4m8.31it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.123981475830078, Poisson: -0.09663776308298111━━━━━━\u001b[0m 40/74 \u001b[2m0:00:05 • 0:00:05\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 24.127620697021484, Poisson: -0.1171291321516037━━━━━━━\u001b[0m 41/74 \u001b[2m0:00:05 • 0:00:04\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 22.49900245666504, Poisson: -0.10819274932146072━━━━━━━\u001b[0m 44/74 \u001b[2m0:00:06 • 0:00:04\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 23.00263786315918, Poisson: -0.110506072640419━━━━━━━━━\u001b[0m 46/74 \u001b[2m0:00:06 • 0:00:04\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
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+      "\u001b[2KMultinomial: 18.882213592529297, Poisson: -0.09008567780256271━━━━━━\u001b[0m 50/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 19.07588768005371, Poisson: -0.0911719799041748━━━━━━━━\u001b[0m 51/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.27490234375, Poisson: -0.10164667665958405━━━━━━━━━━\u001b[0m 52/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.32it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.133913040161133, Poisson: -0.09580332040786743━━━━━━\u001b[0m 53/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.88479995727539, Poisson: -0.10471463203430176━━━━━━━\u001b[0m 54/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 23.608705520629883, Poisson: -0.11381559818983078━━━━━━\u001b[0m 55/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.67367935180664, Poisson: -0.10473752021789551━━━━━━━\u001b[0m 56/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.32634925842285, Poisson: -0.10256417095661163━━━━━━━\u001b[0m 57/74 \u001b[2m0:00:07 • 0:00:03\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 18.426626205444336, Poisson: -0.08782409876585007━━━━━━\u001b[0m 58/74 \u001b[2m0:00:07 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.632646560668945, Poisson: -0.09872711449861526━━━━━━\u001b[0m 59/74 \u001b[2m0:00:08 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.749286651611328, Poisson: -0.09882116317749023━━━━━━\u001b[0m 60/74 \u001b[2m0:00:08 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 24.698978424072266, Poisson: -0.11996717005968094m━━━━━\u001b[0m 61/74 \u001b[2m0:00:08 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 23.38947296142578, Poisson: -0.113921090960502620m━━━━━\u001b[0m 62/74 \u001b[2m0:00:08 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.311670303344727, Poisson: -0.10184700787067413m━━━━━\u001b[0m 63/74 \u001b[2m0:00:08 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 16.17388153076172, Poisson: -0.0760393813252449[90m━━━━\u001b[0m 64/74 \u001b[2m0:00:08 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.14570426940918, Poisson: -0.0968563184142112790m━━━━\u001b[0m 65/74 \u001b[2m0:00:08 • 0:00:02\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.790889739990234, Poisson: -0.0991434305906295890m━━━\u001b[0m 66/74 \u001b[2m0:00:08 • 0:00:01\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.144437789916992, Poisson: -0.1018408611416816790m━━━\u001b[0m 67/74 \u001b[2m0:00:09 • 0:00:01\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 22.674360275268555, Poisson: -0.10741650313138962[90m━━\u001b[0m 68/74 \u001b[2m0:00:09 • 0:00:01\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.273677825927734, Poisson: -0.09590758383274078[90m━━\u001b[0m 69/74 \u001b[2m0:00:09 • 0:00:01\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 17.7067928314209, Poisson: -0.084573782980442050m\u001b[90m━\u001b[0m 70/74 \u001b[2m0:00:09 • 0:00:01\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 21.424694061279297, Poisson: -0.10147197544574738\u001b[90m━\u001b[0m 71/74 \u001b[2m0:00:09 • 0:00:01\u001b[0m \u001b[2;4m8.33it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.870670318603516, Poisson: -0.09882539510726929m 72/74 \u001b[2m0:00:09 • 0:00:01\u001b[0m \u001b[2;4m8.34it/s\u001b[0m \u001b[0m \n",
+      "\u001b[2KMultinomial: 24.119218826293945, Poisson: -0.11633071303367615m 73/74 \u001b[2m0:00:09 • 0:00:01\u001b[0m \u001b[2;4m8.34it/s\u001b[0m \n",
+      "\u001b[2K┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓/74 \u001b[2m0:00:09 • 0:00:00\u001b[0m \u001b[2;4m8.34it/s\u001b[0m \u001b[0m \n",
+      "┃\u001b[1m \u001b[0m\u001b[1m     Validate metric     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
+      "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+      "│\u001b[36m \u001b[0m\u001b[36m    val_gene_pearson     \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m  -0.003818511962890625  \u001b[0m\u001b[35m \u001b[0m│\n",
+      "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    20.72085952758789    \u001b[0m\u001b[35m \u001b[0m│\n",
+      "│\u001b[36m \u001b[0m\u001b[36m         val_mse         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   28.739408493041992    \u001b[0m\u001b[35m \u001b[0m│\n",
+      "│\u001b[36m \u001b[0m\u001b[36m    val_task_pearson     \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m  0.004076463636010885   \u001b[0m\u001b[35m \u001b[0m│\n",
+      "└───────────────────────────┴───────────────────────────┘\n",
+      "\u001b[2KValidation \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 74/74 \u001b[2m0:00:09 • 0:00:00\u001b[0m \u001b[2;4m8.34it/s\u001b[0m \n",
+      "\u001b[?25h/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima finetune --name fi ...\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/pytorch_lightning/utilities/model_summary/model_summary.py:242: UserWarning: Precision 16-mixed is not supported by the model summary.  Estimated model size in MB will not be accurate. Using 32 bits instead.\n",
+      "┏━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
+      "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName           \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                         \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mMode \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mFLOPs\u001b[0m\u001b[1;35m \u001b[0m┃\n",
+      "┡━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
+      "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ model           │ DecimaModel                   │  171 M │ train │     0 │\n",
+      "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ loss            │ TaskWisePoissonMultinomialLo… │      0 │ train │     0 │\n",
+      "│\u001b[2m \u001b[0m\u001b[2m2\u001b[0m\u001b[2m \u001b[0m│ val_metrics     │ MetricCollection              │      0 │ train │     0 │\n",
+      "│\u001b[2m \u001b[0m\u001b[2m3\u001b[0m\u001b[2m \u001b[0m│ test_metrics    │ MetricCollection              │      0 │ train │     0 │\n",
+      "│\u001b[2m \u001b[0m\u001b[2m4\u001b[0m\u001b[2m \u001b[0m│ warning_counter │ WarningCounter                │      0 │ train │     0 │\n",
+      "│\u001b[2m \u001b[0m\u001b[2m5\u001b[0m\u001b[2m \u001b[0m│ transform       │ Identity                      │      0 │ train │     0 │\n",
+      "└───┴─────────────────┴───────────────────────────────┴────────┴───────┴───────┘\n",
+      "\u001b[1mTrainable params\u001b[0m: 171 M                                                         \n",
+      "\u001b[1mNon-trainable params\u001b[0m: 0                                                         \n",
+      "\u001b[1mTotal params\u001b[0m: 171 M                                                             \n",
+      "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 685                                     \n",
+      "\u001b[1mModules in train mode\u001b[0m: 401                                                      \n",
+      "\u001b[1mModules in eval mode\u001b[0m: 0                                                         \n",
+      "\u001b[1mTotal FLOPs\u001b[0m: 0                                                                  \n",
+      "\u001b[2KMultinomial: 17.870079040527344, Poisson: -0.08475557714700699\n",
+      "\u001b[2KMultinomial: 17.161930084228516, Poisson: -0.08194837719202042━━━━━━━━\u001b[0m 1/2 \u001b[2m0:00:00 • -:--:--\u001b[0m \u001b[2;4m0.00it/s\u001b[0m \n",
+      "\u001b[2KMultinomial: 20.591466903686523, Poisson: -0.09825824946165085 • -:--:--\u001b[0m \u001b[2;4m0.00it/s\u001b[0m \u001b[3mv_num: s659\u001b[0m\u001b[0m \n",
+      "\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 1/765 \u001b[2m0:00:00 • -:--:--\u001b[0m \u001b[2;4m0.00it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.215755462646484, Poisson: -0.09574876725673676  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 1/765 \u001b[2m0:00:00 • -:--:--\u001b[0m \u001b[2;4m0.00it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 2/765 \u001b[2m0:00:01 • 0:04:19\u001b[0m \u001b[2;4m2.95it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 2/765 \u001b[2m0:00:01 • 0:04:19\u001b[0m \u001b[2;4m2.95it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.269533157348633, Poisson: -0.10130313783884048  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 2/765 \u001b[2m0:00:01 • 0:04:19\u001b[0m \u001b[2;4m2.95it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 3/765 \u001b[2m0:00:01 • 0:04:31\u001b[0m \u001b[2;4m2.81it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 3/765 \u001b[2m0:00:01 • 0:04:31\u001b[0m \u001b[2;4m2.81it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.106569290161133, Poisson: -0.09563881903886795  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 3/765 \u001b[2m0:00:01 • 0:04:31\u001b[0m \u001b[2;4m2.81it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 4/765 \u001b[2m0:00:01 • 0:04:35\u001b[0m \u001b[2;4m2.77it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 4/765 \u001b[2m0:00:02 • 0:04:35\u001b[0m \u001b[2;4m2.77it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.730449676513672, Poisson: -0.10445458441972733  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 4/765 \u001b[2m0:00:02 • 0:04:35\u001b[0m \u001b[2;4m2.77it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 5/765 \u001b[2m0:00:02 • 0:05:38\u001b[0m \u001b[2;4m2.25it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 5/765 \u001b[2m0:00:02 • 0:05:38\u001b[0m \u001b[2;4m2.25it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.127920150756836, Poisson: -0.0958944782614708   \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 5/765 \u001b[2m0:00:02 • 0:05:38\u001b[0m \u001b[2;4m2.25it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 6/765 \u001b[2m0:00:02 • 0:04:53\u001b[0m \u001b[2;4m2.59it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 6/765 \u001b[2m0:00:02 • 0:04:53\u001b[0m \u001b[2;4m2.59it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.653276443481445, Poisson: -0.09823289513587952  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 6/765 \u001b[2m0:00:02 • 0:04:53\u001b[0m \u001b[2;4m2.59it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 7/765 \u001b[2m0:00:03 • 0:04:51\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 7/765 \u001b[2m0:00:03 • 0:04:51\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.898021697998047, Poisson: -0.11037621647119522  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 7/765 \u001b[2m0:00:03 • 0:04:51\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8/765 \u001b[2m0:00:03 • 0:04:49\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8/765 \u001b[2m0:00:03 • 0:04:49\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.56226348876953, Poisson: -0.09841962903738022   \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8/765 \u001b[2m0:00:03 • 0:04:49\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 9/765 \u001b[2m0:00:03 • 0:04:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 9/765 \u001b[2m0:00:04 • 0:04:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.766332626342773, Poisson: -0.10412967205047607  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 9/765 \u001b[2m0:00:04 • 0:04:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659       \u001b[0m\n",
+      "                                                              \u001b[3mtrain_loss_step:  \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 10/765 \u001b[2m0:00:04 • 0:05:07\u001b[0m \u001b[2;4m2.46it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 10/765 \u001b[2m0:00:04 • 0:05:07\u001b[0m \u001b[2;4m2.46it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.056795120239258, Poisson: -0.09564287960529327  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 10/765 \u001b[2m0:00:04 • 0:05:07\u001b[0m \u001b[2;4m2.46it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 11/765 \u001b[2m0:00:04 • 0:04:48\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 11/765 \u001b[2m0:00:04 • 0:04:48\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.34719467163086, Poisson: -0.08718939870595932   \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 11/765 \u001b[2m0:00:04 • 0:04:48\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 12/765 \u001b[2m0:00:04 • 0:04:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 12/765 \u001b[2m0:00:05 • 0:04:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.68282699584961, Poisson: -0.0983523577451706    \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 12/765 \u001b[2m0:00:05 • 0:04:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 13/765 \u001b[2m0:00:05 • 0:04:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 13/765 \u001b[2m0:00:05 • 0:04:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.080411911010742, Poisson: -0.09565604478120804  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 13/765 \u001b[2m0:00:05 • 0:04:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 14/765 \u001b[2m0:00:05 • 0:04:45\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 14/765 \u001b[2m0:00:05 • 0:04:45\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.103145599365234, Poisson: -0.09544505178928375  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 14/765 \u001b[2m0:00:05 • 0:04:45\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 15/765 \u001b[2m0:00:06 • 0:04:57\u001b[0m \u001b[2;4m2.53it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 15/765 \u001b[2m0:00:06 • 0:04:57\u001b[0m \u001b[2;4m2.53it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.67081069946289, Poisson: -0.11854539811611176   \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 15/765 \u001b[2m0:00:06 • 0:04:57\u001b[0m \u001b[2;4m2.53it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 16/765 \u001b[2m0:00:06 • 0:04:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 16/765 \u001b[2m0:00:06 • 0:04:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.299360275268555, Poisson: -0.10719610005617142  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 16/765 \u001b[2m0:00:06 • 0:04:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 17/765 \u001b[2m0:00:06 • 0:04:44\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 17/765 \u001b[2m0:00:07 • 0:04:44\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.82685089111328, Poisson: -0.10440637171268463   \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 17/765 \u001b[2m0:00:07 • 0:04:44\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 18/765 \u001b[2m0:00:07 • 0:04:43\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 18/765 \u001b[2m0:00:07 • 0:04:43\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.837743759155273, Poisson: -0.10466329008340836  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 18/765 \u001b[2m0:00:07 • 0:04:43\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 19/765 \u001b[2m0:00:07 • 0:04:43\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 19/765 \u001b[2m0:00:07 • 0:04:43\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.312870025634766, Poisson: -0.10132954269647598  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 19/765 \u001b[2m0:00:07 • 0:04:43\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 20/765 \u001b[2m0:00:08 • 0:04:51\u001b[0m \u001b[2;4m2.56it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 20/765 \u001b[2m0:00:08 • 0:04:51\u001b[0m \u001b[2;4m2.56it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.929471969604492, Poisson: -0.10988853126764297  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 20/765 \u001b[2m0:00:08 • 0:04:51\u001b[0m \u001b[2;4m2.56it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 21/765 \u001b[2m0:00:08 • 0:04:42\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 21/765 \u001b[2m0:00:08 • 0:04:42\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.385223388671875, Poisson: -0.10749836266040802  \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 21/765 \u001b[2m0:00:08 • 0:04:42\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 22/765 \u001b[2m0:00:08 • 0:04:42\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 22/765 \u001b[2m0:00:08 • 0:04:42\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.18564224243164, Poisson: -0.0956653356552124    \u001b[0m\n",
+      "Epoch 0/0  \u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 22/765 \u001b[2m0:00:08 • 0:04:42\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 23/765 \u001b[2m0:00:09 • 0:04:41\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 23/765 \u001b[2m0:00:09 • 0:04:41\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.396249771118164, Poisson: -0.10182863473892212  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 23/765 \u001b[2m0:00:09 • 0:04:41\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 24/765 \u001b[2m0:00:09 • 0:04:41\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 24/765 \u001b[2m0:00:09 • 0:04:41\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.8479061126709, Poisson: -0.08977138251066208    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 24/765 \u001b[2m0:00:09 • 0:04:41\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 25/765 \u001b[2m0:00:10 • 0:04:47\u001b[0m \u001b[2;4m2.58it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 25/765 \u001b[2m0:00:10 • 0:04:47\u001b[0m \u001b[2;4m2.58it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.6091365814209, Poisson: -0.09273892641067505    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 25/765 \u001b[2m0:00:10 • 0:04:47\u001b[0m \u001b[2;4m2.58it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 26/765 \u001b[2m0:00:10 • 0:04:40\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 26/765 \u001b[2m0:00:10 • 0:04:40\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.487424850463867, Poisson: -0.09240647405385971  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 26/765 \u001b[2m0:00:10 • 0:04:40\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 27/765 \u001b[2m0:00:10 • 0:04:40\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 27/765 \u001b[2m0:00:10 • 0:04:40\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.084867477416992, Poisson: -0.09545490145683289  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 27/765 \u001b[2m0:00:10 • 0:04:40\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 28/765 \u001b[2m0:00:10 • 0:04:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 28/765 \u001b[2m0:00:11 • 0:04:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.980350494384766, Poisson: -0.11043921858072281  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 28/765 \u001b[2m0:00:11 • 0:04:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 29/765 \u001b[2m0:00:11 • 0:04:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 29/765 \u001b[2m0:00:11 • 0:04:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.722232818603516, Poisson: -0.0986059308052063   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 29/765 \u001b[2m0:00:11 • 0:04:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 30/765 \u001b[2m0:00:11 • 0:04:44\u001b[0m \u001b[2;4m2.59it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 30/765 \u001b[2m0:00:11 • 0:04:44\u001b[0m \u001b[2;4m2.59it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.699106216430664, Poisson: -0.07839540392160416  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 30/765 \u001b[2m0:00:11 • 0:04:44\u001b[0m \u001b[2;4m2.59it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 31/765 \u001b[2m0:00:12 • 0:04:38\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 31/765 \u001b[2m0:00:12 • 0:04:38\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.46723747253418, Poisson: -0.09314889460802078   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 31/765 \u001b[2m0:00:12 • 0:04:38\u001b[0m \u001b[2;4m2.64it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 32/765 \u001b[2m0:00:12 • 0:04:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 32/765 \u001b[2m0:00:12 • 0:04:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.915937423706055, Poisson: -0.08960811793804169  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 32/765 \u001b[2m0:00:12 • 0:04:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 33/765 \u001b[2m0:00:12 • 0:04:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 33/765 \u001b[2m0:00:13 • 0:04:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.35262107849121, Poisson: -0.10728950053453445   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 33/765 \u001b[2m0:00:13 • 0:04:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 34/765 \u001b[2m0:00:13 • 0:04:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 34/765 \u001b[2m0:00:13 • 0:04:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.80293083190918, Poisson: -0.1040886789560318    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 34/765 \u001b[2m0:00:13 • 0:04:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 35/765 \u001b[2m0:00:13 • 0:04:41\u001b[0m \u001b[2;4m2.60it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 35/765 \u001b[2m0:00:13 • 0:04:41\u001b[0m \u001b[2;4m2.60it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.067262649536133, Poisson: -0.0954749584197998   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 35/765 \u001b[2m0:00:13 • 0:04:41\u001b[0m \u001b[2;4m2.60it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 36/765 \u001b[2m0:00:13 • 0:04:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 36/765 \u001b[2m0:00:14 • 0:04:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.39272689819336, Poisson: -0.1077466756105423    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 36/765 \u001b[2m0:00:14 • 0:04:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 37/765 \u001b[2m0:00:14 • 0:04:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 37/765 \u001b[2m0:00:14 • 0:04:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.365856170654297, Poisson: -0.10762537270784378  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 37/765 \u001b[2m0:00:14 • 0:04:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 38/765 \u001b[2m0:00:14 • 0:04:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 38/765 \u001b[2m0:00:14 • 0:04:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.43439483642578, Poisson: -0.09291505068540573   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 38/765 \u001b[2m0:00:14 • 0:04:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 39/765 \u001b[2m0:00:15 • 0:04:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 39/765 \u001b[2m0:00:15 • 0:04:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.413110733032227, Poisson: -0.10738786309957504  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 39/765 \u001b[2m0:00:15 • 0:04:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 40/765 \u001b[2m0:00:15 • 0:04:39\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 40/765 \u001b[2m0:00:15 • 0:04:39\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.801456451416016, Poisson: -0.12415224313735962  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 40/765 \u001b[2m0:00:15 • 0:04:39\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 41/765 \u001b[2m0:00:15 • 0:04:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 41/765 \u001b[2m0:00:16 • 0:04:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.007707595825195, Poisson: -0.0956769585609436   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 41/765 \u001b[2m0:00:16 • 0:04:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 42/765 \u001b[2m0:00:16 • 0:04:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 42/765 \u001b[2m0:00:16 • 0:04:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.223196029663086, Poisson: -0.10163439065217972  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 42/765 \u001b[2m0:00:16 • 0:04:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 43/765 \u001b[2m0:00:16 • 0:04:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 43/765 \u001b[2m0:00:16 • 0:04:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.880022048950195, Poisson: -0.08991735428571701  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 43/765 \u001b[2m0:00:16 • 0:04:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 44/765 \u001b[2m0:00:16 • 0:04:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 44/765 \u001b[2m0:00:17 • 0:04:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.624223709106445, Poisson: -0.09810779988765717  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 44/765 \u001b[2m0:00:17 • 0:04:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 45/765 \u001b[2m0:00:17 • 0:04:36\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 45/765 \u001b[2m0:00:17 • 0:04:36\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.519729614257812, Poisson: -0.09291161596775055  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 45/765 \u001b[2m0:00:17 • 0:04:36\u001b[0m \u001b[2;4m2.61it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 46/765 \u001b[2m0:00:17 • 0:04:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 46/765 \u001b[2m0:00:17 • 0:04:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.56657600402832, Poisson: -0.0985681489109993    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 46/765 \u001b[2m0:00:17 • 0:04:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 47/765 \u001b[2m0:00:18 • 0:04:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 47/765 \u001b[2m0:00:18 • 0:04:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.480541229248047, Poisson: -0.11254521459341049  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 47/765 \u001b[2m0:00:18 • 0:04:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 48/765 \u001b[2m0:00:18 • 0:04:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 48/765 \u001b[2m0:00:18 • 0:04:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.743654251098633, Poisson: -0.0843479111790657   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 48/765 \u001b[2m0:00:18 • 0:04:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 49/765 \u001b[2m0:00:18 • 0:04:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 49/765 \u001b[2m0:00:19 • 0:04:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.717634201049805, Poisson: -0.10403894633054733  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 49/765 \u001b[2m0:00:19 • 0:04:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 50/765 \u001b[2m0:00:19 • 0:04:34\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 50/765 \u001b[2m0:00:19 • 0:04:34\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.164640426635742, Poisson: -0.10177622735500336  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 50/765 \u001b[2m0:00:19 • 0:04:34\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 51/765 \u001b[2m0:00:19 • 0:04:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 51/765 \u001b[2m0:00:19 • 0:04:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.116952896118164, Poisson: -0.09567848592996597  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 51/765 \u001b[2m0:00:19 • 0:04:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 52/765 \u001b[2m0:00:20 • 0:04:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 52/765 \u001b[2m0:00:20 • 0:04:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.295698165893555, Poisson: -0.08694960922002792  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 52/765 \u001b[2m0:00:20 • 0:04:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 53/765 \u001b[2m0:00:20 • 0:04:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 53/765 \u001b[2m0:00:20 • 0:04:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.939062118530273, Poisson: -0.11040302366018295  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 53/765 \u001b[2m0:00:20 • 0:04:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 54/765 \u001b[2m0:00:20 • 0:04:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 54/765 \u001b[2m0:00:20 • 0:04:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.48210334777832, Poisson: -0.11263477802276611   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 54/765 \u001b[2m0:00:20 • 0:04:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 55/765 \u001b[2m0:00:21 • 0:04:31\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 55/765 \u001b[2m0:00:21 • 0:04:31\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.47770118713379, Poisson: -0.11274239420890808   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 55/765 \u001b[2m0:00:21 • 0:04:31\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 56/765 \u001b[2m0:00:21 • 0:04:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 56/765 \u001b[2m0:00:21 • 0:04:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.621450424194336, Poisson: -0.09877743571996689  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 56/765 \u001b[2m0:00:21 • 0:04:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 57/765 \u001b[2m0:00:21 • 0:04:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 57/765 \u001b[2m0:00:22 • 0:04:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.45052146911621, Poisson: -0.0926215723156929    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 57/765 \u001b[2m0:00:22 • 0:04:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 58/765 \u001b[2m0:00:22 • 0:04:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 58/765 \u001b[2m0:00:22 • 0:04:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.6368408203125, Poisson: -0.09874047338962555    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 58/765 \u001b[2m0:00:22 • 0:04:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 59/765 \u001b[2m0:00:22 • 0:04:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 59/765 \u001b[2m0:00:22 • 0:04:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.56623077392578, Poisson: -0.0985851138830185    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 59/765 \u001b[2m0:00:22 • 0:04:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 60/765 \u001b[2m0:00:23 • 0:04:29\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 60/765 \u001b[2m0:00:23 • 0:04:29\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.90228271484375, Poisson: -0.1102643683552742    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 60/765 \u001b[2m0:00:23 • 0:04:29\u001b[0m \u001b[2;4m2.62it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 61/765 \u001b[2m0:00:23 • 0:04:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 61/765 \u001b[2m0:00:23 • 0:04:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.3968448638916, Poisson: -0.09321321547031403    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 61/765 \u001b[2m0:00:23 • 0:04:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 62/765 \u001b[2m0:00:23 • 0:04:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 62/765 \u001b[2m0:00:24 • 0:04:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.790693283081055, Poisson: -0.10399416089057922  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 62/765 \u001b[2m0:00:24 • 0:04:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 63/765 \u001b[2m0:00:24 • 0:04:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 63/765 \u001b[2m0:00:24 • 0:04:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.03215980529785, Poisson: -0.09596872329711914   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 63/765 \u001b[2m0:00:24 • 0:04:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 64/765 \u001b[2m0:00:24 • 0:04:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 64/765 \u001b[2m0:00:24 • 0:04:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.814613342285156, Poisson: -0.10461093485355377  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 64/765 \u001b[2m0:00:24 • 0:04:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 65/765 \u001b[2m0:00:25 • 0:04:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 65/765 \u001b[2m0:00:25 • 0:04:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.18915557861328, Poisson: -0.10160321742296219   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 65/765 \u001b[2m0:00:25 • 0:04:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 66/765 \u001b[2m0:00:25 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 66/765 \u001b[2m0:00:25 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.422182083129883, Poisson: -0.09316802769899368  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 66/765 \u001b[2m0:00:25 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 67/765 \u001b[2m0:00:25 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 67/765 \u001b[2m0:00:25 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.65222930908203, Poisson: -0.09896299988031387   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 67/765 \u001b[2m0:00:25 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 68/765 \u001b[2m0:00:26 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 68/765 \u001b[2m0:00:26 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.032657623291016, Poisson: -0.0955779030919075   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 68/765 \u001b[2m0:00:26 • 0:04:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 69/765 \u001b[2m0:00:26 • 0:04:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 69/765 \u001b[2m0:00:26 • 0:04:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.780378341674805, Poisson: -0.10465734452009201  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 69/765 \u001b[2m0:00:26 • 0:04:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 70/765 \u001b[2m0:00:27 • 0:04:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 70/765 \u001b[2m0:00:27 • 0:04:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.014028549194336, Poisson: -0.11038114875555038  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 70/765 \u001b[2m0:00:27 • 0:04:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 71/765 \u001b[2m0:00:27 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 71/765 \u001b[2m0:00:27 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.489234924316406, Poisson: -0.09303180873394012  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 71/765 \u001b[2m0:00:27 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 72/765 \u001b[2m0:00:27 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 72/765 \u001b[2m0:00:27 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.56780242919922, Poisson: -0.09307807683944702   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 72/765 \u001b[2m0:00:27 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 73/765 \u001b[2m0:00:27 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 73/765 \u001b[2m0:00:28 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.17972183227539, Poisson: -0.10181150585412979   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 73/765 \u001b[2m0:00:28 • 0:04:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 74/765 \u001b[2m0:00:28 • 0:04:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 74/765 \u001b[2m0:00:28 • 0:04:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.233455657958984, Poisson: -0.10176549106836319  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 74/765 \u001b[2m0:00:28 • 0:04:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 75/765 \u001b[2m0:00:28 • 0:04:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 75/765 \u001b[2m0:00:28 • 0:04:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.908485412597656, Poisson: -0.0900127962231636   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 75/765 \u001b[2m0:00:28 • 0:04:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 76/765 \u001b[2m0:00:29 • 0:04:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 76/765 \u001b[2m0:00:29 • 0:04:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.491857528686523, Poisson: -0.11326884478330612  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 76/765 \u001b[2m0:00:29 • 0:04:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 77/765 \u001b[2m0:00:29 • 0:04:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 77/765 \u001b[2m0:00:29 • 0:04:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.168283462524414, Poisson: -0.12169722467660904  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 77/765 \u001b[2m0:00:29 • 0:04:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 78/765 \u001b[2m0:00:29 • 0:04:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 78/765 \u001b[2m0:00:30 • 0:04:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.222675323486328, Poisson: -0.1019572764635086   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 78/765 \u001b[2m0:00:30 • 0:04:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 79/765 \u001b[2m0:00:30 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 79/765 \u001b[2m0:00:30 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.781164169311523, Poisson: -0.1046329215168953   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 79/765 \u001b[2m0:00:30 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 80/765 \u001b[2m0:00:30 • 0:04:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 80/765 \u001b[2m0:00:30 • 0:04:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.640405654907227, Poisson: -0.09894818812608719  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 80/765 \u001b[2m0:00:30 • 0:04:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 81/765 \u001b[2m0:00:30 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 81/765 \u001b[2m0:00:31 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.335317611694336, Poisson: -0.10747018456459045  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 81/765 \u001b[2m0:00:31 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 82/765 \u001b[2m0:00:31 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 82/765 \u001b[2m0:00:31 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.475839614868164, Poisson: -0.09266920387744904  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 82/765 \u001b[2m0:00:31 • 0:04:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 83/765 \u001b[2m0:00:31 • 0:04:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 83/765 \u001b[2m0:00:31 • 0:04:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.61337661743164, Poisson: -0.09915477782487869   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 83/765 \u001b[2m0:00:31 • 0:04:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 84/765 \u001b[2m0:00:32 • 0:04:15\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 84/765 \u001b[2m0:00:32 • 0:04:15\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90678596496582, Poisson: -0.08999187499284744   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 84/765 \u001b[2m0:00:32 • 0:04:15\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 85/765 \u001b[2m0:00:32 • 0:04:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 85/765 \u001b[2m0:00:32 • 0:04:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.167694091796875, Poisson: -0.1015259176492691   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 85/765 \u001b[2m0:00:32 • 0:04:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 86/765 \u001b[2m0:00:32 • 0:04:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 86/765 \u001b[2m0:00:33 • 0:04:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.244850158691406, Poisson: -0.10156884789466858  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 86/765 \u001b[2m0:00:33 • 0:04:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 87/765 \u001b[2m0:00:33 • 0:04:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 87/765 \u001b[2m0:00:33 • 0:04:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.47123908996582, Poisson: -0.09322365373373032   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 87/765 \u001b[2m0:00:33 • 0:04:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 88/765 \u001b[2m0:00:33 • 0:04:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 88/765 \u001b[2m0:00:33 • 0:04:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.702177047729492, Poisson: -0.10465401411056519  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 88/765 \u001b[2m0:00:33 • 0:04:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 89/765 \u001b[2m0:00:33 • 0:04:13\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 89/765 \u001b[2m0:00:34 • 0:04:13\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.35836410522461, Poisson: -0.10769332200288773   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 89/765 \u001b[2m0:00:34 • 0:04:13\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 90/765 \u001b[2m0:00:34 • 0:04:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 90/765 \u001b[2m0:00:34 • 0:04:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.873342514038086, Poisson: -0.0902843102812767   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 90/765 \u001b[2m0:00:34 • 0:04:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 91/765 \u001b[2m0:00:34 • 0:04:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 91/765 \u001b[2m0:00:34 • 0:04:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.21904945373535, Poisson: -0.10175640136003494   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 91/765 \u001b[2m0:00:34 • 0:04:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 92/765 \u001b[2m0:00:35 • 0:04:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 92/765 \u001b[2m0:00:35 • 0:04:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.05417251586914, Poisson: -0.1162276342511177    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 92/765 \u001b[2m0:00:35 • 0:04:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 93/765 \u001b[2m0:00:35 • 0:04:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 93/765 \u001b[2m0:00:35 • 0:04:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.708969116210938, Poisson: -0.08441831171512604  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 93/765 \u001b[2m0:00:35 • 0:04:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 94/765 \u001b[2m0:00:35 • 0:04:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 94/765 \u001b[2m0:00:36 • 0:04:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.05093002319336, Poisson: -0.09540779888629913   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 94/765 \u001b[2m0:00:36 • 0:04:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 95/765 \u001b[2m0:00:36 • 0:04:15\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 95/765 \u001b[2m0:00:36 • 0:04:15\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.792226791381836, Poisson: -0.10469885170459747  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 95/765 \u001b[2m0:00:36 • 0:04:15\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 96/765 \u001b[2m0:00:36 • 0:04:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 96/765 \u001b[2m0:00:36 • 0:04:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.95119285583496, Poisson: -0.09018448740243912   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 96/765 \u001b[2m0:00:36 • 0:04:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 97/765 \u001b[2m0:00:36 • 0:04:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 97/765 \u001b[2m0:00:37 • 0:04:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.209503173828125, Poisson: -0.10166148096323013  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 97/765 \u001b[2m0:00:37 • 0:04:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 98/765 \u001b[2m0:00:37 • 0:04:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 98/765 \u001b[2m0:00:37 • 0:04:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.525936126708984, Poisson: -0.09305062890052795  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 98/765 \u001b[2m0:00:37 • 0:04:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 99/765 \u001b[2m0:00:37 • 0:04:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 99/765 \u001b[2m0:00:37 • 0:04:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.52171516418457, Poisson: -0.09302598983049393   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 99/765 \u001b[2m0:00:37 • 0:04:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 100/765 \u001b[2m0:00:38 • 0:04:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 100/765 \u001b[2m0:00:38 • 0:04:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.970510482788086, Poisson: -0.0959007516503334   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 100/765 \u001b[2m0:00:38 • 0:04:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 101/765 \u001b[2m0:00:38 • 0:04:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 101/765 \u001b[2m0:00:38 • 0:04:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.207353591918945, Poisson: -0.10139843076467514  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 101/765 \u001b[2m0:00:38 • 0:04:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 102/765 \u001b[2m0:00:38 • 0:04:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 102/765 \u001b[2m0:00:39 • 0:04:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.77320671081543, Poisson: -0.1046697124838829    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 102/765 \u001b[2m0:00:39 • 0:04:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 103/765 \u001b[2m0:00:39 • 0:04:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 103/765 \u001b[2m0:00:39 • 0:04:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.74834442138672, Poisson: -0.10458948463201523   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 103/765 \u001b[2m0:00:39 • 0:04:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 104/765 \u001b[2m0:00:39 • 0:04:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 104/765 \u001b[2m0:00:39 • 0:04:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.23369789123535, Poisson: -0.12206012010574341   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 104/765 \u001b[2m0:00:39 • 0:04:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 105/765 \u001b[2m0:00:40 • 0:04:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 105/765 \u001b[2m0:00:40 • 0:04:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.49113655090332, Poisson: -0.113157257437706     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 105/765 \u001b[2m0:00:40 • 0:04:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 106/765 \u001b[2m0:00:40 • 0:04:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 106/765 \u001b[2m0:00:40 • 0:04:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.37995147705078, Poisson: -0.08720425516366959   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 106/765 \u001b[2m0:00:40 • 0:04:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 107/765 \u001b[2m0:00:40 • 0:04:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 107/765 \u001b[2m0:00:41 • 0:04:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.3621768951416, Poisson: -0.10741036385297775    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 107/765 \u001b[2m0:00:41 • 0:04:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 108/765 \u001b[2m0:00:41 • 0:04:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 108/765 \u001b[2m0:00:41 • 0:04:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.27471160888672, Poisson: -0.10737208276987076   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 108/765 \u001b[2m0:00:41 • 0:04:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 109/765 \u001b[2m0:00:41 • 0:04:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 109/765 \u001b[2m0:00:41 • 0:04:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.332916259765625, Poisson: -0.087388776242733    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 109/765 \u001b[2m0:00:41 • 0:04:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 110/765 \u001b[2m0:00:42 • 0:04:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 110/765 \u001b[2m0:00:42 • 0:04:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.625844955444336, Poisson: -0.09861352294683456  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 110/765 \u001b[2m0:00:42 • 0:04:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 111/765 \u001b[2m0:00:42 • 0:04:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 111/765 \u001b[2m0:00:42 • 0:04:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.622392654418945, Poisson: -0.09872077405452728  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 111/765 \u001b[2m0:00:42 • 0:04:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 112/765 \u001b[2m0:00:42 • 0:04:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 112/765 \u001b[2m0:00:42 • 0:04:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.874479293823242, Poisson: -0.08997733891010284  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 112/765 \u001b[2m0:00:42 • 0:04:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 113/765 \u001b[2m0:00:43 • 0:04:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 113/765 \u001b[2m0:00:43 • 0:04:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.512983322143555, Poisson: -0.09290290623903275  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 113/765 \u001b[2m0:00:43 • 0:04:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 114/765 \u001b[2m0:00:43 • 0:04:04\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 114/765 \u001b[2m0:00:43 • 0:04:04\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.225149154663086, Poisson: -0.08122935146093369  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 114/765 \u001b[2m0:00:43 • 0:04:04\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 115/765 \u001b[2m0:00:44 • 0:04:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 115/765 \u001b[2m0:00:44 • 0:04:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.194129943847656, Poisson: -0.10167499631643295  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 115/765 \u001b[2m0:00:44 • 0:04:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 116/765 \u001b[2m0:00:44 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 116/765 \u001b[2m0:00:44 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.213668823242188, Poisson: -0.10166061669588089  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 116/765 \u001b[2m0:00:44 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 117/765 \u001b[2m0:00:44 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 117/765 \u001b[2m0:00:44 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.882850646972656, Poisson: -0.10988707095384598  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 117/765 \u001b[2m0:00:44 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 118/765 \u001b[2m0:00:44 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 118/765 \u001b[2m0:00:45 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.35940933227539, Poisson: -0.1076277568936348    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 118/765 \u001b[2m0:00:45 • 0:04:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 119/765 \u001b[2m0:00:45 • 0:04:02\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 119/765 \u001b[2m0:00:45 • 0:04:02\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.898441314697266, Poisson: -0.0899832472205162   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 119/765 \u001b[2m0:00:45 • 0:04:02\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 120/765 \u001b[2m0:00:45 • 0:04:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 120/765 \u001b[2m0:00:45 • 0:04:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.459415435791016, Poisson: -0.11315363645553589  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 120/765 \u001b[2m0:00:45 • 0:04:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 121/765 \u001b[2m0:00:46 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 121/765 \u001b[2m0:00:46 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.44660186767578, Poisson: -0.11297596246004105   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 121/765 \u001b[2m0:00:46 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 122/765 \u001b[2m0:00:46 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 122/765 \u001b[2m0:00:46 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.21309471130371, Poisson: -0.10170062631368637   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 122/765 \u001b[2m0:00:46 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 123/765 \u001b[2m0:00:46 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 123/765 \u001b[2m0:00:47 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.303546905517578, Poisson: -0.10745315998792648  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 123/765 \u001b[2m0:00:47 • 0:04:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 124/765 \u001b[2m0:00:47 • 0:04:00\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 124/765 \u001b[2m0:00:47 • 0:04:00\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.06381607055664, Poisson: -0.09579093754291534   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 124/765 \u001b[2m0:00:47 • 0:04:00\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 125/765 \u001b[2m0:00:47 • 0:04:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 125/765 \u001b[2m0:00:47 • 0:04:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.94003677368164, Poisson: -0.0899321660399437    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 125/765 \u001b[2m0:00:47 • 0:04:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 126/765 \u001b[2m0:00:47 • 0:04:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 126/765 \u001b[2m0:00:48 • 0:04:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.768007278442383, Poisson: -0.1043400764465332   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 126/765 \u001b[2m0:00:48 • 0:04:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 127/765 \u001b[2m0:00:48 • 0:04:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 127/765 \u001b[2m0:00:48 • 0:04:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.946666717529297, Poisson: -0.11041778326034546  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 127/765 \u001b[2m0:00:48 • 0:04:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 128/765 \u001b[2m0:00:48 • 0:04:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 128/765 \u001b[2m0:00:48 • 0:04:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.641420364379883, Poisson: -0.09869152307510376  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 128/765 \u001b[2m0:00:48 • 0:04:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 129/765 \u001b[2m0:00:49 • 0:03:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 129/765 \u001b[2m0:00:49 • 0:03:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.87653160095215, Poisson: -0.09020334482192993   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 129/765 \u001b[2m0:00:49 • 0:03:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 130/765 \u001b[2m0:00:49 • 0:04:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 130/765 \u001b[2m0:00:49 • 0:04:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.482999801635742, Poisson: -0.11302231252193451  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 130/765 \u001b[2m0:00:49 • 0:04:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 131/765 \u001b[2m0:00:49 • 0:04:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 131/765 \u001b[2m0:00:50 • 0:04:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.02503204345703, Poisson: -0.09583611786365509   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 131/765 \u001b[2m0:00:50 • 0:04:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 132/765 \u001b[2m0:00:50 • 0:03:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 132/765 \u001b[2m0:00:50 • 0:03:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.18289566040039, Poisson: -0.10137318819761276   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 132/765 \u001b[2m0:00:50 • 0:03:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 133/765 \u001b[2m0:00:50 • 0:03:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 133/765 \u001b[2m0:00:50 • 0:03:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.770231246948242, Poisson: -0.10451383888721466  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 133/765 \u001b[2m0:00:50 • 0:03:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 134/765 \u001b[2m0:00:50 • 0:03:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 134/765 \u001b[2m0:00:51 • 0:03:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.86833381652832, Poisson: -0.09007661044597626   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 134/765 \u001b[2m0:00:51 • 0:03:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 135/765 \u001b[2m0:00:51 • 0:04:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 135/765 \u001b[2m0:00:51 • 0:04:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.744047164916992, Poisson: -0.10458433628082275  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 135/765 \u001b[2m0:00:51 • 0:04:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 136/765 \u001b[2m0:00:51 • 0:03:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 136/765 \u001b[2m0:00:51 • 0:03:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.395092010498047, Poisson: -0.09292133897542953  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 136/765 \u001b[2m0:00:51 • 0:03:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 137/765 \u001b[2m0:00:52 • 0:03:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 137/765 \u001b[2m0:00:52 • 0:03:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.063337326049805, Poisson: -0.11590410768985748  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 137/765 \u001b[2m0:00:52 • 0:03:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 138/765 \u001b[2m0:00:52 • 0:03:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 138/765 \u001b[2m0:00:52 • 0:03:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.218862533569336, Poisson: -0.10160845518112183  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 138/765 \u001b[2m0:00:52 • 0:03:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 139/765 \u001b[2m0:00:52 • 0:03:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 139/765 \u001b[2m0:00:53 • 0:03:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.152191162109375, Poisson: -0.08129559457302094  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 139/765 \u001b[2m0:00:53 • 0:03:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 140/765 \u001b[2m0:00:53 • 0:03:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 140/765 \u001b[2m0:00:53 • 0:03:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.939743041992188, Poisson: -0.1095491275191307   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 140/765 \u001b[2m0:00:53 • 0:03:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 141/765 \u001b[2m0:00:53 • 0:03:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 141/765 \u001b[2m0:00:53 • 0:03:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.573505401611328, Poisson: -0.07815657556056976  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 141/765 \u001b[2m0:00:53 • 0:03:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 142/765 \u001b[2m0:00:53 • 0:03:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 142/765 \u001b[2m0:00:54 • 0:03:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.331560134887695, Poisson: -0.10716519504785538  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 142/765 \u001b[2m0:00:54 • 0:03:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 143/765 \u001b[2m0:00:54 • 0:03:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 143/765 \u001b[2m0:00:54 • 0:03:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.638803482055664, Poisson: -0.09873533993959427  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 143/765 \u001b[2m0:00:54 • 0:03:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 144/765 \u001b[2m0:00:54 • 0:03:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 144/765 \u001b[2m0:00:54 • 0:03:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.776708602905273, Poisson: -0.10455451160669327  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 144/765 \u001b[2m0:00:54 • 0:03:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 145/765 \u001b[2m0:00:55 • 0:03:56\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 145/765 \u001b[2m0:00:55 • 0:03:56\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.152740478515625, Poisson: -0.10155022144317627  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 145/765 \u001b[2m0:00:55 • 0:03:56\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 146/765 \u001b[2m0:00:55 • 0:03:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 146/765 \u001b[2m0:00:55 • 0:03:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.989484786987305, Poisson: -0.095651775598526    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 146/765 \u001b[2m0:00:55 • 0:03:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 147/765 \u001b[2m0:00:55 • 0:03:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 147/765 \u001b[2m0:00:56 • 0:03:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.335159301757812, Poisson: -0.08703257143497467  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 147/765 \u001b[2m0:00:56 • 0:03:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 148/765 \u001b[2m0:00:56 • 0:03:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 148/765 \u001b[2m0:00:56 • 0:03:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.89045524597168, Poisson: -0.08982937037944794   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 148/765 \u001b[2m0:00:56 • 0:03:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 149/765 \u001b[2m0:00:56 • 0:03:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 149/765 \u001b[2m0:00:56 • 0:03:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.26311683654785, Poisson: -0.10133222490549088   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 149/765 \u001b[2m0:00:56 • 0:03:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 150/765 \u001b[2m0:00:57 • 0:03:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 150/765 \u001b[2m0:00:57 • 0:03:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.77498435974121, Poisson: -0.10429241508245468   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 150/765 \u001b[2m0:00:57 • 0:03:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 151/765 \u001b[2m0:00:57 • 0:03:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 151/765 \u001b[2m0:00:57 • 0:03:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.23119354248047, Poisson: -0.10148240625858307   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 151/765 \u001b[2m0:00:57 • 0:03:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 152/765 \u001b[2m0:00:57 • 0:03:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 152/765 \u001b[2m0:00:57 • 0:03:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.78129768371582, Poisson: -0.10432910919189453   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 152/765 \u001b[2m0:00:57 • 0:03:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 153/765 \u001b[2m0:00:58 • 0:03:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 153/765 \u001b[2m0:00:58 • 0:03:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.70320701599121, Poisson: -0.08425717055797577   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 153/765 \u001b[2m0:00:58 • 0:03:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 154/765 \u001b[2m0:00:58 • 0:03:49\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 154/765 \u001b[2m0:00:58 • 0:03:49\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.62248420715332, Poisson: -0.09888274222612381   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 154/765 \u001b[2m0:00:58 • 0:03:49\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 155/765 \u001b[2m0:00:59 • 0:03:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 155/765 \u001b[2m0:00:59 • 0:03:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.035017013549805, Poisson: -0.11595393717288971  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 155/765 \u001b[2m0:00:59 • 0:03:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 156/765 \u001b[2m0:00:59 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 156/765 \u001b[2m0:00:59 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.46041488647461, Poisson: -0.09247714281082153   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 156/765 \u001b[2m0:00:59 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 157/765 \u001b[2m0:00:59 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 157/765 \u001b[2m0:00:59 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.04898452758789, Poisson: -0.11585573107004166   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 157/765 \u001b[2m0:00:59 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 158/765 \u001b[2m0:01:00 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 158/765 \u001b[2m0:01:00 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.847970962524414, Poisson: -0.08996086567640305  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 158/765 \u001b[2m0:01:00 • 0:03:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 159/765 \u001b[2m0:01:00 • 0:03:47\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 159/765 \u001b[2m0:01:00 • 0:03:47\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.88404083251953, Poisson: -0.08998685330152512   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 159/765 \u001b[2m0:01:00 • 0:03:47\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 160/765 \u001b[2m0:01:00 • 0:03:51\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 160/765 \u001b[2m0:01:01 • 0:03:51\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.306896209716797, Poisson: -0.08686136454343796  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 160/765 \u001b[2m0:01:01 • 0:03:51\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 161/765 \u001b[2m0:01:01 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 161/765 \u001b[2m0:01:01 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.92879295349121, Poisson: -0.08990245312452316   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 161/765 \u001b[2m0:01:01 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 162/765 \u001b[2m0:01:01 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 162/765 \u001b[2m0:01:01 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.88294792175293, Poisson: -0.10999883711338043   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 162/765 \u001b[2m0:01:01 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 163/765 \u001b[2m0:01:01 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 163/765 \u001b[2m0:01:02 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.95570945739746, Poisson: -0.11020589619874954   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 163/765 \u001b[2m0:01:02 • 0:03:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 164/765 \u001b[2m0:01:02 • 0:03:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 164/765 \u001b[2m0:01:02 • 0:03:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.738420486450195, Poisson: -0.08428659290075302  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 164/765 \u001b[2m0:01:02 • 0:03:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 165/765 \u001b[2m0:01:02 • 0:03:49\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 165/765 \u001b[2m0:01:02 • 0:03:49\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.0937442779541, Poisson: -0.07546532899141312    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 165/765 \u001b[2m0:01:02 • 0:03:49\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 166/765 \u001b[2m0:01:03 • 0:03:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 166/765 \u001b[2m0:01:03 • 0:03:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.366117477416992, Poisson: -0.08690521866083145  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 166/765 \u001b[2m0:01:03 • 0:03:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 167/765 \u001b[2m0:01:03 • 0:03:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 167/765 \u001b[2m0:01:03 • 0:03:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.47005271911621, Poisson: -0.09279369562864304   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 167/765 \u001b[2m0:01:03 • 0:03:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 168/765 \u001b[2m0:01:03 • 0:03:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 168/765 \u001b[2m0:01:04 • 0:03:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.5909423828125, Poisson: -0.09841950237751007    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 168/765 \u001b[2m0:01:04 • 0:03:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 169/765 \u001b[2m0:01:04 • 0:03:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 169/765 \u001b[2m0:01:04 • 0:03:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.30916404724121, Poisson: -0.08688811957836151   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 169/765 \u001b[2m0:01:04 • 0:03:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 170/765 \u001b[2m0:01:04 • 0:03:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 170/765 \u001b[2m0:01:04 • 0:03:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.056997299194336, Poisson: -0.11580225825309753  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 170/765 \u001b[2m0:01:04 • 0:03:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 171/765 \u001b[2m0:01:04 • 0:03:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 171/765 \u001b[2m0:01:05 • 0:03:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.748809814453125, Poisson: -0.1042604073882103   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 171/765 \u001b[2m0:01:05 • 0:03:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 172/765 \u001b[2m0:01:05 • 0:03:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 172/765 \u001b[2m0:01:05 • 0:03:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.893999099731445, Poisson: -0.11014977842569351  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 172/765 \u001b[2m0:01:05 • 0:03:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 173/765 \u001b[2m0:01:05 • 0:03:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 173/765 \u001b[2m0:01:05 • 0:03:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.020978927612305, Poisson: -0.09547249227762222  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 173/765 \u001b[2m0:01:05 • 0:03:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 174/765 \u001b[2m0:01:06 • 0:03:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 174/765 \u001b[2m0:01:06 • 0:03:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.013200759887695, Poisson: -0.09576494246721268  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 174/765 \u001b[2m0:01:06 • 0:03:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 175/765 \u001b[2m0:01:06 • 0:03:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 175/765 \u001b[2m0:01:06 • 0:03:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.17913818359375, Poisson: -0.12204756587743759   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 175/765 \u001b[2m0:01:06 • 0:03:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 176/765 \u001b[2m0:01:06 • 0:03:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 176/765 \u001b[2m0:01:07 • 0:03:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.15540313720703, Poisson: -0.1013311967253685    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 176/765 \u001b[2m0:01:07 • 0:03:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 177/765 \u001b[2m0:01:07 • 0:03:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 177/765 \u001b[2m0:01:07 • 0:03:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.881107330322266, Poisson: -0.09007158130407333  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 177/765 \u001b[2m0:01:07 • 0:03:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 178/765 \u001b[2m0:01:07 • 0:03:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 178/765 \u001b[2m0:01:07 • 0:03:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.2780704498291, Poisson: -0.08695535361766815    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 178/765 \u001b[2m0:01:07 • 0:03:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 179/765 \u001b[2m0:01:07 • 0:03:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 179/765 \u001b[2m0:01:08 • 0:03:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.75191879272461, Poisson: -0.08422835916280746   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 179/765 \u001b[2m0:01:08 • 0:03:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 180/765 \u001b[2m0:01:08 • 0:03:43\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 180/765 \u001b[2m0:01:08 • 0:03:43\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.08552360534668, Poisson: -0.09560148417949677   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 180/765 \u001b[2m0:01:08 • 0:03:43\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 181/765 \u001b[2m0:01:08 • 0:03:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 181/765 \u001b[2m0:01:08 • 0:03:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.41364288330078, Poisson: -0.09282485395669937   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 181/765 \u001b[2m0:01:08 • 0:03:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 182/765 \u001b[2m0:01:09 • 0:03:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 182/765 \u001b[2m0:01:09 • 0:03:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.99930763244629, Poisson: -0.09562334418296814   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 182/765 \u001b[2m0:01:09 • 0:03:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 183/765 \u001b[2m0:01:09 • 0:03:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 183/765 \u001b[2m0:01:09 • 0:03:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.095056533813477, Poisson: -0.09571366757154465  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 183/765 \u001b[2m0:01:09 • 0:03:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 184/765 \u001b[2m0:01:09 • 0:03:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 184/765 \u001b[2m0:01:10 • 0:03:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.433696746826172, Poisson: -0.09290359914302826  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 184/765 \u001b[2m0:01:10 • 0:03:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 185/765 \u001b[2m0:01:10 • 0:03:41\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 185/765 \u001b[2m0:01:10 • 0:03:41\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.09649658203125, Poisson: -0.095783531665802     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 185/765 \u001b[2m0:01:10 • 0:03:41\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 186/765 \u001b[2m0:01:10 • 0:03:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 186/765 \u001b[2m0:01:10 • 0:03:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.315677642822266, Poisson: -0.10725086182355881  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 186/765 \u001b[2m0:01:10 • 0:03:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 187/765 \u001b[2m0:01:10 • 0:03:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 187/765 \u001b[2m0:01:11 • 0:03:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.171001434326172, Poisson: -0.1014031171798706   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 187/765 \u001b[2m0:01:11 • 0:03:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 188/765 \u001b[2m0:01:11 • 0:03:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 188/765 \u001b[2m0:01:11 • 0:03:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.634620666503906, Poisson: -0.09867609292268753  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 188/765 \u001b[2m0:01:11 • 0:03:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 189/765 \u001b[2m0:01:11 • 0:03:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 189/765 \u001b[2m0:01:11 • 0:03:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90178871154785, Poisson: -0.09006387740373611   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 189/765 \u001b[2m0:01:11 • 0:03:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 190/765 \u001b[2m0:01:12 • 0:03:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 190/765 \u001b[2m0:01:12 • 0:03:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.01470184326172, Poisson: -0.07559339702129364   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 190/765 \u001b[2m0:01:12 • 0:03:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 191/765 \u001b[2m0:01:12 • 0:03:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 191/765 \u001b[2m0:01:12 • 0:03:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.91354751586914, Poisson: -0.089971624314785     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 191/765 \u001b[2m0:01:12 • 0:03:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 192/765 \u001b[2m0:01:12 • 0:03:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 192/765 \u001b[2m0:01:13 • 0:03:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.59370231628418, Poisson: -0.09880663454532623   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 192/765 \u001b[2m0:01:13 • 0:03:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 193/765 \u001b[2m0:01:13 • 0:03:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 193/765 \u001b[2m0:01:13 • 0:03:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.890140533447266, Poisson: -0.11017723381519318  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 193/765 \u001b[2m0:01:13 • 0:03:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 194/765 \u001b[2m0:01:13 • 0:03:34\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 194/765 \u001b[2m0:01:13 • 0:03:34\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.15829849243164, Poisson: -0.10154072940349579   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 194/765 \u001b[2m0:01:13 • 0:03:34\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 195/765 \u001b[2m0:01:14 • 0:03:37\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 195/765 \u001b[2m0:01:14 • 0:03:37\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.14064598083496, Poisson: -0.101683109998703     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 195/765 \u001b[2m0:01:14 • 0:03:37\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 196/765 \u001b[2m0:01:14 • 0:03:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 196/765 \u001b[2m0:01:14 • 0:03:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.74208641052246, Poisson: -0.10457500070333481   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 196/765 \u001b[2m0:01:14 • 0:03:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 197/765 \u001b[2m0:01:14 • 0:03:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 197/765 \u001b[2m0:01:14 • 0:03:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.303442001342773, Poisson: -0.10707332193851471  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 197/765 \u001b[2m0:01:14 • 0:03:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 198/765 \u001b[2m0:01:15 • 0:03:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 198/765 \u001b[2m0:01:15 • 0:03:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.2916202545166, Poisson: -0.0869942456483841     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 198/765 \u001b[2m0:01:15 • 0:03:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 199/765 \u001b[2m0:01:15 • 0:03:32\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 199/765 \u001b[2m0:01:15 • 0:03:32\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.787240982055664, Poisson: -0.10447119176387787  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 199/765 \u001b[2m0:01:15 • 0:03:32\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 200/765 \u001b[2m0:01:16 • 0:03:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 200/765 \u001b[2m0:01:16 • 0:03:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.671932220458984, Poisson: -0.07837022095918655  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 200/765 \u001b[2m0:01:16 • 0:03:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 201/765 \u001b[2m0:01:16 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 201/765 \u001b[2m0:01:16 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.305335998535156, Poisson: -0.10727039724588394  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 201/765 \u001b[2m0:01:16 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 202/765 \u001b[2m0:01:16 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 202/765 \u001b[2m0:01:16 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.780101776123047, Poisson: -0.10442784428596497  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 202/765 \u001b[2m0:01:16 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 203/765 \u001b[2m0:01:16 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 203/765 \u001b[2m0:01:17 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.615991592407227, Poisson: -0.09867089986801147  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 203/765 \u001b[2m0:01:17 • 0:03:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 204/765 \u001b[2m0:01:17 • 0:03:30\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 204/765 \u001b[2m0:01:17 • 0:03:30\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.03567123413086, Poisson: -0.09567002207040787   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 204/765 \u001b[2m0:01:17 • 0:03:30\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 205/765 \u001b[2m0:01:17 • 0:03:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 205/765 \u001b[2m0:01:17 • 0:03:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.740985870361328, Poisson: -0.103847436606884    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 205/765 \u001b[2m0:01:17 • 0:03:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 206/765 \u001b[2m0:01:18 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 206/765 \u001b[2m0:01:18 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.606483459472656, Poisson: -0.09882691502571106  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 206/765 \u001b[2m0:01:18 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 207/765 \u001b[2m0:01:18 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 207/765 \u001b[2m0:01:18 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.92123794555664, Poisson: -0.08995452523231506   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 207/765 \u001b[2m0:01:18 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 208/765 \u001b[2m0:01:18 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 208/765 \u001b[2m0:01:19 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.030040740966797, Poisson: -0.0957757979631424   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 208/765 \u001b[2m0:01:19 • 0:03:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 209/765 \u001b[2m0:01:19 • 0:03:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 209/765 \u001b[2m0:01:19 • 0:03:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.045814514160156, Poisson: -0.09591474384069443  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 209/765 \u001b[2m0:01:19 • 0:03:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 210/765 \u001b[2m0:01:19 • 0:03:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 210/765 \u001b[2m0:01:19 • 0:03:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.40960693359375, Poisson: -0.11301376670598984   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 210/765 \u001b[2m0:01:19 • 0:03:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 211/765 \u001b[2m0:01:20 • 0:03:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 211/765 \u001b[2m0:01:20 • 0:03:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.749469757080078, Poisson: -0.1043415293097496   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 211/765 \u001b[2m0:01:20 • 0:03:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 212/765 \u001b[2m0:01:20 • 0:03:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 212/765 \u001b[2m0:01:20 • 0:03:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.413198471069336, Poisson: -0.09301866590976715  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 212/765 \u001b[2m0:01:20 • 0:03:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 213/765 \u001b[2m0:01:20 • 0:03:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 213/765 \u001b[2m0:01:20 • 0:03:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.498214721679688, Poisson: -0.1134181022644043   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 213/765 \u001b[2m0:01:20 • 0:03:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 214/765 \u001b[2m0:01:21 • 0:03:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 214/765 \u001b[2m0:01:21 • 0:03:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.36588478088379, Poisson: -0.08717481791973114   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 214/765 \u001b[2m0:01:21 • 0:03:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 215/765 \u001b[2m0:01:21 • 0:03:30\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 215/765 \u001b[2m0:01:21 • 0:03:30\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.722591400146484, Poisson: -0.10465965420007706  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 215/765 \u001b[2m0:01:21 • 0:03:30\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 216/765 \u001b[2m0:01:21 • 0:03:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 216/765 \u001b[2m0:01:22 • 0:03:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.566452026367188, Poisson: -0.09877440333366394  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 216/765 \u001b[2m0:01:22 • 0:03:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 217/765 \u001b[2m0:01:22 • 0:03:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 217/765 \u001b[2m0:01:22 • 0:03:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.070781707763672, Poisson: -0.0958305299282074   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 217/765 \u001b[2m0:01:22 • 0:03:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 218/765 \u001b[2m0:01:22 • 0:03:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 218/765 \u001b[2m0:01:22 • 0:03:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.44015884399414, Poisson: -0.09297657757997513   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 218/765 \u001b[2m0:01:22 • 0:03:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 219/765 \u001b[2m0:01:23 • 0:03:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 219/765 \u001b[2m0:01:23 • 0:03:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.043834686279297, Poisson: -0.095887191593647    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 219/765 \u001b[2m0:01:23 • 0:03:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 220/765 \u001b[2m0:01:23 • 0:03:28\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 220/765 \u001b[2m0:01:23 • 0:03:28\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.32887840270996, Poisson: -0.10742559283971786   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 220/765 \u001b[2m0:01:23 • 0:03:28\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 221/765 \u001b[2m0:01:23 • 0:03:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 221/765 \u001b[2m0:01:24 • 0:03:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.32686996459961, Poisson: -0.10768169909715652   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 221/765 \u001b[2m0:01:24 • 0:03:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 222/765 \u001b[2m0:01:24 • 0:03:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 222/765 \u001b[2m0:01:24 • 0:03:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.03035545349121, Poisson: -0.09575172513723373   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 222/765 \u001b[2m0:01:24 • 0:03:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 223/765 \u001b[2m0:01:24 • 0:03:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 223/765 \u001b[2m0:01:24 • 0:03:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.34048843383789, Poisson: -0.10737773030996323   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 223/765 \u001b[2m0:01:24 • 0:03:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 224/765 \u001b[2m0:01:24 • 0:03:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 224/765 \u001b[2m0:01:25 • 0:03:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.075281143188477, Poisson: -0.11586207896471024  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 224/765 \u001b[2m0:01:25 • 0:03:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 225/765 \u001b[2m0:01:25 • 0:03:26\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 225/765 \u001b[2m0:01:25 • 0:03:26\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.286996841430664, Poisson: -0.10753754526376724  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 225/765 \u001b[2m0:01:25 • 0:03:26\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 226/765 \u001b[2m0:01:25 • 0:03:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 226/765 \u001b[2m0:01:25 • 0:03:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.338281631469727, Poisson: -0.10757587850093842  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 226/765 \u001b[2m0:01:25 • 0:03:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 227/765 \u001b[2m0:01:26 • 0:03:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 227/765 \u001b[2m0:01:26 • 0:03:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.950529098510742, Poisson: -0.09600374847650528  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 227/765 \u001b[2m0:01:26 • 0:03:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 228/765 \u001b[2m0:01:26 • 0:03:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 228/765 \u001b[2m0:01:26 • 0:03:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.298465728759766, Poisson: -0.08730553090572357  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 228/765 \u001b[2m0:01:26 • 0:03:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 229/765 \u001b[2m0:01:26 • 0:03:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 229/765 \u001b[2m0:01:27 • 0:03:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.361501693725586, Poisson: -0.10756424069404602  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 229/765 \u001b[2m0:01:27 • 0:03:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 230/765 \u001b[2m0:01:27 • 0:03:24\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 230/765 \u001b[2m0:01:27 • 0:03:24\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.488300323486328, Poisson: -0.09318099170923233  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 230/765 \u001b[2m0:01:27 • 0:03:24\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 231/765 \u001b[2m0:01:27 • 0:03:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 231/765 \u001b[2m0:01:27 • 0:03:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90535545349121, Poisson: -0.09025786072015762   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 231/765 \u001b[2m0:01:27 • 0:03:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 232/765 \u001b[2m0:01:27 • 0:03:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 232/765 \u001b[2m0:01:28 • 0:03:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.97938346862793, Poisson: -0.09601100534200668   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 232/765 \u001b[2m0:01:28 • 0:03:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 233/765 \u001b[2m0:01:28 • 0:03:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 233/765 \u001b[2m0:01:28 • 0:03:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.5343017578125, Poisson: -0.09316454082727432    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 233/765 \u001b[2m0:01:28 • 0:03:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 234/765 \u001b[2m0:01:28 • 0:03:19\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 234/765 \u001b[2m0:01:28 • 0:03:19\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.438522338867188, Poisson: -0.09322395920753479  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 234/765 \u001b[2m0:01:28 • 0:03:19\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 235/765 \u001b[2m0:01:29 • 0:03:22\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 235/765 \u001b[2m0:01:29 • 0:03:22\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.94364356994629, Poisson: -0.11059822887182236   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 235/765 \u001b[2m0:01:29 • 0:03:22\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 236/765 \u001b[2m0:01:29 • 0:03:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 236/765 \u001b[2m0:01:29 • 0:03:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.071102142333984, Poisson: -0.09607089310884476  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 236/765 \u001b[2m0:01:29 • 0:03:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 237/765 \u001b[2m0:01:29 • 0:03:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 237/765 \u001b[2m0:01:30 • 0:03:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90325164794922, Poisson: -0.09031011164188385   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 237/765 \u001b[2m0:01:30 • 0:03:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 238/765 \u001b[2m0:01:30 • 0:03:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 238/765 \u001b[2m0:01:30 • 0:03:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.85365104675293, Poisson: -0.11047089844942093   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 238/765 \u001b[2m0:01:30 • 0:03:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 239/765 \u001b[2m0:01:30 • 0:03:17\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 239/765 \u001b[2m0:01:30 • 0:03:17\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.782655715942383, Poisson: -0.08447491377592087  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 239/765 \u001b[2m0:01:30 • 0:03:17\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 240/765 \u001b[2m0:01:31 • 0:03:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 240/765 \u001b[2m0:01:31 • 0:03:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.605976104736328, Poisson: -0.09889329224824905  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 240/765 \u001b[2m0:01:31 • 0:03:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 241/765 \u001b[2m0:01:31 • 0:03:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 241/765 \u001b[2m0:01:31 • 0:03:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.674373626708984, Poisson: -0.0990610346198082   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 241/765 \u001b[2m0:01:31 • 0:03:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 242/765 \u001b[2m0:01:31 • 0:03:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 242/765 \u001b[2m0:01:31 • 0:03:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.771625518798828, Poisson: -0.104603610932827    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 242/765 \u001b[2m0:01:31 • 0:03:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 243/765 \u001b[2m0:01:32 • 0:03:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 243/765 \u001b[2m0:01:32 • 0:03:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.035152435302734, Poisson: -0.1162971779704094   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 243/765 \u001b[2m0:01:32 • 0:03:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 244/765 \u001b[2m0:01:32 • 0:03:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 244/765 \u001b[2m0:01:32 • 0:03:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.592443466186523, Poisson: -0.0989699587225914   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 244/765 \u001b[2m0:01:32 • 0:03:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 245/765 \u001b[2m0:01:33 • 0:03:18\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 245/765 \u001b[2m0:01:33 • 0:03:18\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.914596557617188, Poisson: -0.11080020666122437  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 245/765 \u001b[2m0:01:33 • 0:03:18\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 246/765 \u001b[2m0:01:33 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 246/765 \u001b[2m0:01:33 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.20119285583496, Poisson: -0.08160963654518127   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 246/765 \u001b[2m0:01:33 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 247/765 \u001b[2m0:01:33 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 247/765 \u001b[2m0:01:33 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.161884307861328, Poisson: -0.10191914439201355  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 247/765 \u001b[2m0:01:33 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 248/765 \u001b[2m0:01:33 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 248/765 \u001b[2m0:01:34 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.437496185302734, Poisson: -0.09330902993679047  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 248/765 \u001b[2m0:01:34 • 0:03:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 249/765 \u001b[2m0:01:34 • 0:03:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 249/765 \u001b[2m0:01:34 • 0:03:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.48818016052246, Poisson: -0.09318145364522934   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 249/765 \u001b[2m0:01:34 • 0:03:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 250/765 \u001b[2m0:01:34 • 0:03:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 250/765 \u001b[2m0:01:34 • 0:03:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.1884765625, Poisson: -0.10206077992916107       \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 250/765 \u001b[2m0:01:34 • 0:03:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 251/765 \u001b[2m0:01:35 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 251/765 \u001b[2m0:01:35 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.895965576171875, Poisson: -0.09033375233411789  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 251/765 \u001b[2m0:01:35 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 252/765 \u001b[2m0:01:35 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 252/765 \u001b[2m0:01:35 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.32925033569336, Poisson: -0.08746848255395889   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 252/765 \u001b[2m0:01:35 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 253/765 \u001b[2m0:01:35 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 253/765 \u001b[2m0:01:36 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.255189895629883, Poisson: -0.10198451578617096  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 253/765 \u001b[2m0:01:36 • 0:03:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 254/765 \u001b[2m0:01:36 • 0:03:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 254/765 \u001b[2m0:01:36 • 0:03:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.7751407623291, Poisson: -0.08454731106758118    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 254/765 \u001b[2m0:01:36 • 0:03:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 255/765 \u001b[2m0:01:36 • 0:03:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 255/765 \u001b[2m0:01:36 • 0:03:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.5142879486084, Poisson: -0.09325987845659256    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 255/765 \u001b[2m0:01:36 • 0:03:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 256/765 \u001b[2m0:01:36 • 0:03:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 256/765 \u001b[2m0:01:37 • 0:03:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.972089767456055, Poisson: -0.09612832963466644  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 256/765 \u001b[2m0:01:37 • 0:03:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 257/765 \u001b[2m0:01:37 • 0:03:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 257/765 \u001b[2m0:01:37 • 0:03:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.463390350341797, Poisson: -0.09319175779819489  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 257/765 \u001b[2m0:01:37 • 0:03:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 258/765 \u001b[2m0:01:37 • 0:03:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 258/765 \u001b[2m0:01:37 • 0:03:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.034791946411133, Poisson: -0.09619896858930588  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 258/765 \u001b[2m0:01:37 • 0:03:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 259/765 \u001b[2m0:01:38 • 0:03:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 259/765 \u001b[2m0:01:38 • 0:03:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.80955696105957, Poisson: -0.09044074267148972   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 259/765 \u001b[2m0:01:38 • 0:03:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 260/765 \u001b[2m0:01:38 • 0:03:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 260/765 \u001b[2m0:01:38 • 0:03:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.932058334350586, Poisson: -0.11060158163309097  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 260/765 \u001b[2m0:01:38 • 0:03:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 261/765 \u001b[2m0:01:38 • 0:03:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 261/765 \u001b[2m0:01:39 • 0:03:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.16964340209961, Poisson: -0.10199590772390366   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 261/765 \u001b[2m0:01:39 • 0:03:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 262/765 \u001b[2m0:01:39 • 0:03:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 262/765 \u001b[2m0:01:39 • 0:03:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.288652420043945, Poisson: -0.08751168102025986  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 262/765 \u001b[2m0:01:39 • 0:03:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 263/765 \u001b[2m0:01:39 • 0:03:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 263/765 \u001b[2m0:01:39 • 0:03:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.175344467163086, Poisson: -0.08161116391420364  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 263/765 \u001b[2m0:01:39 • 0:03:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 264/765 \u001b[2m0:01:39 • 0:03:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 264/765 \u001b[2m0:01:40 • 0:03:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.901798248291016, Poisson: -0.09028562903404236  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 264/765 \u001b[2m0:01:40 • 0:03:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 265/765 \u001b[2m0:01:40 • 0:03:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 265/765 \u001b[2m0:01:40 • 0:03:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.2131290435791, Poisson: -0.08174476027488708    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 265/765 \u001b[2m0:01:40 • 0:03:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 266/765 \u001b[2m0:01:40 • 0:03:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 266/765 \u001b[2m0:01:40 • 0:03:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.64095115661621, Poisson: -0.09908179938793182   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 266/765 \u001b[2m0:01:40 • 0:03:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 267/765 \u001b[2m0:01:41 • 0:03:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 267/765 \u001b[2m0:01:41 • 0:03:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.401880264282227, Poisson: -0.10790243744850159  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 267/765 \u001b[2m0:01:41 • 0:03:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 268/765 \u001b[2m0:01:41 • 0:03:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 268/765 \u001b[2m0:01:41 • 0:03:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.649999618530273, Poisson: -0.09919221699237823  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 268/765 \u001b[2m0:01:41 • 0:03:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 269/765 \u001b[2m0:01:41 • 0:03:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 269/765 \u001b[2m0:01:42 • 0:03:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.66010856628418, Poisson: -0.09918005019426346   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 269/765 \u001b[2m0:01:42 • 0:03:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 270/765 \u001b[2m0:01:42 • 0:03:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 270/765 \u001b[2m0:01:42 • 0:03:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.345069885253906, Poisson: -0.1078648567199707   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 270/765 \u001b[2m0:01:42 • 0:03:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 271/765 \u001b[2m0:01:42 • 0:03:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 271/765 \u001b[2m0:01:42 • 0:03:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.23171043395996, Poisson: -0.12242132425308228   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 271/765 \u001b[2m0:01:42 • 0:03:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 272/765 \u001b[2m0:01:43 • 0:03:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 272/765 \u001b[2m0:01:43 • 0:03:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.250429153442383, Poisson: -0.10207130759954453  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 272/765 \u001b[2m0:01:43 • 0:03:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 273/765 \u001b[2m0:01:43 • 0:03:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 273/765 \u001b[2m0:01:43 • 0:03:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.334081649780273, Poisson: -0.10789871215820312  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 273/765 \u001b[2m0:01:43 • 0:03:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 274/765 \u001b[2m0:01:43 • 0:03:04\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 274/765 \u001b[2m0:01:44 • 0:03:04\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.73067283630371, Poisson: -0.10490907728672028   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 274/765 \u001b[2m0:01:44 • 0:03:04\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 275/765 \u001b[2m0:01:44 • 0:03:07\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 275/765 \u001b[2m0:01:44 • 0:03:07\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.057233810424805, Poisson: -0.09630308300256729  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 275/765 \u001b[2m0:01:44 • 0:03:07\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 276/765 \u001b[2m0:01:44 • 0:03:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 276/765 \u001b[2m0:01:44 • 0:03:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.0758113861084, Poisson: -0.09628889709711075    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 276/765 \u001b[2m0:01:44 • 0:03:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 277/765 \u001b[2m0:01:44 • 0:03:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 277/765 \u001b[2m0:01:45 • 0:03:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.05191421508789, Poisson: -0.09625305235385895   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 277/765 \u001b[2m0:01:45 • 0:03:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 278/765 \u001b[2m0:01:45 • 0:03:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 278/765 \u001b[2m0:01:45 • 0:03:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.60567855834961, Poisson: -0.09914851188659668   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 278/765 \u001b[2m0:01:45 • 0:03:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 279/765 \u001b[2m0:01:45 • 0:03:02\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 279/765 \u001b[2m0:01:45 • 0:03:02\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.949203491210938, Poisson: -0.11037476360797882  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 279/765 \u001b[2m0:01:45 • 0:03:02\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 280/765 \u001b[2m0:01:46 • 0:03:05\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 280/765 \u001b[2m0:01:46 • 0:03:05\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.335355758666992, Poisson: -0.10794167220592499  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 280/765 \u001b[2m0:01:46 • 0:03:05\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 281/765 \u001b[2m0:01:46 • 0:03:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 281/765 \u001b[2m0:01:46 • 0:03:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.61472511291504, Poisson: -0.11940038949251175   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 281/765 \u001b[2m0:01:46 • 0:03:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 282/765 \u001b[2m0:01:46 • 0:03:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 282/765 \u001b[2m0:01:47 • 0:03:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.696815490722656, Poisson: -0.0846056416630745   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 282/765 \u001b[2m0:01:47 • 0:03:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 283/765 \u001b[2m0:01:47 • 0:03:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 283/765 \u001b[2m0:01:47 • 0:03:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.333906173706055, Poisson: -0.08748296648263931  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 283/765 \u001b[2m0:01:47 • 0:03:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 284/765 \u001b[2m0:01:47 • 0:03:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 284/765 \u001b[2m0:01:47 • 0:03:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.6047306060791, Poisson: -0.09916960448026657    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 284/765 \u001b[2m0:01:47 • 0:03:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 285/765 \u001b[2m0:01:48 • 0:03:03\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 285/765 \u001b[2m0:01:48 • 0:03:03\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.35996437072754, Poisson: -0.10788378864526749   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 285/765 \u001b[2m0:01:48 • 0:03:03\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 286/765 \u001b[2m0:01:48 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 286/765 \u001b[2m0:01:48 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.640731811523438, Poisson: -0.09906243532896042  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 286/765 \u001b[2m0:01:48 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 287/765 \u001b[2m0:01:48 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 287/765 \u001b[2m0:01:48 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.339624404907227, Poisson: -0.1078748106956482   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 287/765 \u001b[2m0:01:48 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 288/765 \u001b[2m0:01:49 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 288/765 \u001b[2m0:01:49 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.723846435546875, Poisson: -0.08462792634963989  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 288/765 \u001b[2m0:01:49 • 0:03:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 289/765 \u001b[2m0:01:49 • 0:02:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 289/765 \u001b[2m0:01:49 • 0:02:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.047983169555664, Poisson: -0.09627726674079895  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 289/765 \u001b[2m0:01:49 • 0:02:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 290/765 \u001b[2m0:01:50 • 0:03:01\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 290/765 \u001b[2m0:01:50 • 0:03:01\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.78467559814453, Poisson: -0.0846315398812294    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 290/765 \u001b[2m0:01:50 • 0:03:01\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 291/765 \u001b[2m0:01:50 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 291/765 \u001b[2m0:01:50 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.16231918334961, Poisson: -0.10205434262752533   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 291/765 \u001b[2m0:01:50 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 292/765 \u001b[2m0:01:50 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 292/765 \u001b[2m0:01:50 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.598783493041992, Poisson: -0.09919189661741257  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 292/765 \u001b[2m0:01:50 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 293/765 \u001b[2m0:01:50 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 293/765 \u001b[2m0:01:51 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.226789474487305, Poisson: -0.1022041067481041   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 293/765 \u001b[2m0:01:51 • 0:02:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 294/765 \u001b[2m0:01:51 • 0:02:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 294/765 \u001b[2m0:01:51 • 0:02:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.4766845703125, Poisson: -0.09333187341690063    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 294/765 \u001b[2m0:01:51 • 0:02:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 295/765 \u001b[2m0:01:51 • 0:02:59\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 295/765 \u001b[2m0:01:51 • 0:02:59\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.333797454833984, Poisson: -0.1078532412648201   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 295/765 \u001b[2m0:01:51 • 0:02:59\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 296/765 \u001b[2m0:01:52 • 0:02:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 296/765 \u001b[2m0:01:52 • 0:02:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.35396957397461, Poisson: -0.10787491500377655   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 296/765 \u001b[2m0:01:52 • 0:02:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 297/765 \u001b[2m0:01:52 • 0:02:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 297/765 \u001b[2m0:01:52 • 0:02:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.75189971923828, Poisson: -0.10501044988632202   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 297/765 \u001b[2m0:01:52 • 0:02:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 298/765 \u001b[2m0:01:52 • 0:02:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 298/765 \u001b[2m0:01:53 • 0:02:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.485422134399414, Poisson: -0.09340836852788925  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 298/765 \u001b[2m0:01:53 • 0:02:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 299/765 \u001b[2m0:01:53 • 0:02:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 299/765 \u001b[2m0:01:53 • 0:02:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.708253860473633, Poisson: -0.08463050425052643  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 299/765 \u001b[2m0:01:53 • 0:02:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 300/765 \u001b[2m0:01:53 • 0:02:57\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 300/765 \u001b[2m0:01:53 • 0:02:57\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.239627838134766, Poisson: -0.10209409892559052  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 300/765 \u001b[2m0:01:53 • 0:02:57\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 301/765 \u001b[2m0:01:53 • 0:02:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 301/765 \u001b[2m0:01:54 • 0:02:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.330678939819336, Poisson: -0.08749771863222122  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 301/765 \u001b[2m0:01:54 • 0:02:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 302/765 \u001b[2m0:01:54 • 0:02:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 302/765 \u001b[2m0:01:54 • 0:02:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.748788833618164, Poisson: -0.08465765416622162  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 302/765 \u001b[2m0:01:54 • 0:02:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 303/765 \u001b[2m0:01:54 • 0:02:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 303/765 \u001b[2m0:01:54 • 0:02:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.75536346435547, Poisson: -0.10500773787498474   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 303/765 \u001b[2m0:01:54 • 0:02:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 304/765 \u001b[2m0:01:55 • 0:02:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 304/765 \u001b[2m0:01:55 • 0:02:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.09800910949707, Poisson: -0.09634219855070114   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 304/765 \u001b[2m0:01:55 • 0:02:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 305/765 \u001b[2m0:01:55 • 0:02:55\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 305/765 \u001b[2m0:01:55 • 0:02:55\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.615209579467773, Poisson: -0.07877222448587418  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 305/765 \u001b[2m0:01:55 • 0:02:55\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 306/765 \u001b[2m0:01:55 • 0:02:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 306/765 \u001b[2m0:01:56 • 0:02:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.35785675048828, Poisson: -0.08757851272821426   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 306/765 \u001b[2m0:01:56 • 0:02:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 307/765 \u001b[2m0:01:56 • 0:02:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 307/765 \u001b[2m0:01:56 • 0:02:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.38044548034668, Poisson: -0.09330804646015167   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 307/765 \u001b[2m0:01:56 • 0:02:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 308/765 \u001b[2m0:01:56 • 0:02:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 308/765 \u001b[2m0:01:56 • 0:02:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.200119018554688, Poisson: -0.10199937224388123  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 308/765 \u001b[2m0:01:56 • 0:02:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 309/765 \u001b[2m0:01:56 • 0:02:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 309/765 \u001b[2m0:01:57 • 0:02:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.613750457763672, Poisson: -0.09918731451034546  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 309/765 \u001b[2m0:01:57 • 0:02:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 310/765 \u001b[2m0:01:57 • 0:02:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 310/765 \u001b[2m0:01:57 • 0:02:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.80516815185547, Poisson: -0.08463717252016068   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 310/765 \u001b[2m0:01:57 • 0:02:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 311/765 \u001b[2m0:01:57 • 0:02:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 311/765 \u001b[2m0:01:57 • 0:02:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.285797119140625, Poisson: -0.08761964738368988  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 311/765 \u001b[2m0:01:57 • 0:02:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 312/765 \u001b[2m0:01:58 • 0:02:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 312/765 \u001b[2m0:01:58 • 0:02:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.326765060424805, Poisson: -0.10791569203138351  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 312/765 \u001b[2m0:01:58 • 0:02:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 313/765 \u001b[2m0:01:58 • 0:02:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 313/765 \u001b[2m0:01:58 • 0:02:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.7198429107666, Poisson: -0.105129674077034      \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 313/765 \u001b[2m0:01:58 • 0:02:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 314/765 \u001b[2m0:01:58 • 0:02:49\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 314/765 \u001b[2m0:01:59 • 0:02:49\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.725595474243164, Poisson: -0.10497389733791351  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 314/765 \u001b[2m0:01:59 • 0:02:49\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 315/765 \u001b[2m0:01:59 • 0:02:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 315/765 \u001b[2m0:01:59 • 0:02:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.860084533691406, Poisson: -0.10497232526540756  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 315/765 \u001b[2m0:01:59 • 0:02:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 316/765 \u001b[2m0:01:59 • 0:02:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 316/765 \u001b[2m0:01:59 • 0:02:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.46031379699707, Poisson: -0.09348584711551666   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 316/765 \u001b[2m0:01:59 • 0:02:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 317/765 \u001b[2m0:02:00 • 0:02:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 317/765 \u001b[2m0:02:00 • 0:02:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.436969757080078, Poisson: -0.09338469058275223  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 317/765 \u001b[2m0:02:00 • 0:02:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 318/765 \u001b[2m0:02:00 • 0:02:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 318/765 \u001b[2m0:02:00 • 0:02:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.36768913269043, Poisson: -0.1077839583158493    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 318/765 \u001b[2m0:02:00 • 0:02:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 319/765 \u001b[2m0:02:00 • 0:02:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 319/765 \u001b[2m0:02:00 • 0:02:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.904165267944336, Poisson: -0.09059745073318481  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 319/765 \u001b[2m0:02:00 • 0:02:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 320/765 \u001b[2m0:02:01 • 0:02:50\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 320/765 \u001b[2m0:02:01 • 0:02:50\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.244510650634766, Poisson: -0.1019989624619484   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 320/765 \u001b[2m0:02:01 • 0:02:50\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 321/765 \u001b[2m0:02:01 • 0:02:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 321/765 \u001b[2m0:02:01 • 0:02:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.279077529907227, Poisson: -0.0875072032213211   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 321/765 \u001b[2m0:02:01 • 0:02:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 322/765 \u001b[2m0:02:01 • 0:02:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 322/765 \u001b[2m0:02:02 • 0:02:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.64546775817871, Poisson: -0.09923851490020752   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 322/765 \u001b[2m0:02:02 • 0:02:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 323/765 \u001b[2m0:02:02 • 0:02:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 323/765 \u001b[2m0:02:02 • 0:02:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.31422996520996, Poisson: -0.08747466653585434   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 323/765 \u001b[2m0:02:02 • 0:02:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 324/765 \u001b[2m0:02:02 • 0:02:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 324/765 \u001b[2m0:02:02 • 0:02:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.63677978515625, Poisson: -0.09926402568817139   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 324/765 \u001b[2m0:02:02 • 0:02:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 325/765 \u001b[2m0:02:03 • 0:02:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 325/765 \u001b[2m0:02:03 • 0:02:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.625150680541992, Poisson: -0.09910394996404648  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 325/765 \u001b[2m0:02:03 • 0:02:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 326/765 \u001b[2m0:02:03 • 0:02:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 326/765 \u001b[2m0:02:03 • 0:02:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.61687469482422, Poisson: -0.09920184314250946   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 326/765 \u001b[2m0:02:03 • 0:02:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 327/765 \u001b[2m0:02:03 • 0:02:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 327/765 \u001b[2m0:02:04 • 0:02:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.02855682373047, Poisson: -0.09613651782274246   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 327/765 \u001b[2m0:02:04 • 0:02:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 328/765 \u001b[2m0:02:04 • 0:02:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 328/765 \u001b[2m0:02:04 • 0:02:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.161123275756836, Poisson: -0.10194823890924454  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 328/765 \u001b[2m0:02:04 • 0:02:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 329/765 \u001b[2m0:02:04 • 0:02:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 329/765 \u001b[2m0:02:04 • 0:02:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.44812774658203, Poisson: -0.09336211532354355   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 329/765 \u001b[2m0:02:04 • 0:02:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 330/765 \u001b[2m0:02:05 • 0:02:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 330/765 \u001b[2m0:02:05 • 0:02:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.908830642700195, Poisson: -0.09040990471839905  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 330/765 \u001b[2m0:02:05 • 0:02:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 331/765 \u001b[2m0:02:05 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 331/765 \u001b[2m0:02:05 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.010530471801758, Poisson: -0.09628235548734665  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 331/765 \u001b[2m0:02:05 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 332/765 \u001b[2m0:02:05 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 332/765 \u001b[2m0:02:05 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.963714599609375, Poisson: -0.11083722859621048  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 332/765 \u001b[2m0:02:05 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 333/765 \u001b[2m0:02:06 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 333/765 \u001b[2m0:02:06 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 14.90597152709961, Poisson: -0.07008238136768341   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 333/765 \u001b[2m0:02:06 • 0:02:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 334/765 \u001b[2m0:02:06 • 0:02:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 334/765 \u001b[2m0:02:06 • 0:02:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.09711456298828, Poisson: -0.09624631702899933   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 334/765 \u001b[2m0:02:06 • 0:02:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 335/765 \u001b[2m0:02:07 • 0:02:44\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 335/765 \u001b[2m0:02:07 • 0:02:44\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.702611923217773, Poisson: -0.1050051599740982   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 335/765 \u001b[2m0:02:07 • 0:02:44\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 336/765 \u001b[2m0:02:07 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 336/765 \u001b[2m0:02:07 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.73402214050293, Poisson: -0.10491544008255005   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 336/765 \u001b[2m0:02:07 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 337/765 \u001b[2m0:02:07 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 337/765 \u001b[2m0:02:07 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.681100845336914, Poisson: -0.1049724817276001   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 337/765 \u001b[2m0:02:07 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 338/765 \u001b[2m0:02:07 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 338/765 \u001b[2m0:02:08 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.17693328857422, Poisson: -0.10215218365192413   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 338/765 \u001b[2m0:02:08 • 0:02:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 339/765 \u001b[2m0:02:08 • 0:02:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 339/765 \u001b[2m0:02:08 • 0:02:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.073955535888672, Poisson: -0.0962051972746849   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 339/765 \u001b[2m0:02:08 • 0:02:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 340/765 \u001b[2m0:02:08 • 0:02:42\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 340/765 \u001b[2m0:02:08 • 0:02:42\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.95260238647461, Poisson: -0.11021999269723892   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 340/765 \u001b[2m0:02:08 • 0:02:42\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 341/765 \u001b[2m0:02:09 • 0:02:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 341/765 \u001b[2m0:02:09 • 0:02:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.208993911743164, Poisson: -0.10199598968029022  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 341/765 \u001b[2m0:02:09 • 0:02:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 342/765 \u001b[2m0:02:09 • 0:02:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 342/765 \u001b[2m0:02:09 • 0:02:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.754135131835938, Poisson: -0.1048768013715744   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 342/765 \u001b[2m0:02:09 • 0:02:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 343/765 \u001b[2m0:02:09 • 0:02:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 343/765 \u001b[2m0:02:10 • 0:02:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.708425521850586, Poisson: -0.1048574447631836   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 343/765 \u001b[2m0:02:10 • 0:02:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 344/765 \u001b[2m0:02:10 • 0:02:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 344/765 \u001b[2m0:02:10 • 0:02:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.865802764892578, Poisson: -0.11070571094751358  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 344/765 \u001b[2m0:02:10 • 0:02:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 345/765 \u001b[2m0:02:10 • 0:02:40\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 345/765 \u001b[2m0:02:10 • 0:02:40\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.037689208984375, Poisson: -0.11661232262849808  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 345/765 \u001b[2m0:02:10 • 0:02:40\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 346/765 \u001b[2m0:02:10 • 0:02:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 346/765 \u001b[2m0:02:11 • 0:02:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.313915252685547, Poisson: -0.10777386277914047  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 346/765 \u001b[2m0:02:11 • 0:02:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 347/765 \u001b[2m0:02:11 • 0:02:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 347/765 \u001b[2m0:02:11 • 0:02:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.217554092407227, Poisson: -0.10213707387447357  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 347/765 \u001b[2m0:02:11 • 0:02:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 348/765 \u001b[2m0:02:11 • 0:02:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 348/765 \u001b[2m0:02:11 • 0:02:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.58501434326172, Poisson: -0.09909378737211227   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 348/765 \u001b[2m0:02:11 • 0:02:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 349/765 \u001b[2m0:02:12 • 0:02:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 349/765 \u001b[2m0:02:12 • 0:02:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.330402374267578, Poisson: -0.10777313262224197  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 349/765 \u001b[2m0:02:12 • 0:02:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 350/765 \u001b[2m0:02:12 • 0:02:38\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 350/765 \u001b[2m0:02:12 • 0:02:38\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.996597290039062, Poisson: -0.09619171172380447  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 350/765 \u001b[2m0:02:12 • 0:02:38\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 351/765 \u001b[2m0:02:12 • 0:02:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 351/765 \u001b[2m0:02:13 • 0:02:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.587121963500977, Poisson: -0.0788431167602539   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 351/765 \u001b[2m0:02:13 • 0:02:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 352/765 \u001b[2m0:02:13 • 0:02:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 352/765 \u001b[2m0:02:13 • 0:02:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.01539421081543, Poisson: -0.09628826379776001   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 352/765 \u001b[2m0:02:13 • 0:02:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 353/765 \u001b[2m0:02:13 • 0:02:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 353/765 \u001b[2m0:02:13 • 0:02:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.052555084228516, Poisson: -0.09646513313055038  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 353/765 \u001b[2m0:02:13 • 0:02:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 354/765 \u001b[2m0:02:13 • 0:02:34\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 354/765 \u001b[2m0:02:14 • 0:02:34\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.36686897277832, Poisson: -0.10788238048553467   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 354/765 \u001b[2m0:02:14 • 0:02:34\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 355/765 \u001b[2m0:02:14 • 0:02:36\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 355/765 \u001b[2m0:02:14 • 0:02:36\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.990596771240234, Poisson: -0.09616326540708542  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 355/765 \u001b[2m0:02:14 • 0:02:36\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 356/765 \u001b[2m0:02:14 • 0:02:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 356/765 \u001b[2m0:02:14 • 0:02:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.316091537475586, Poisson: -0.0875689685344696   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 356/765 \u001b[2m0:02:14 • 0:02:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 357/765 \u001b[2m0:02:15 • 0:02:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 357/765 \u001b[2m0:02:15 • 0:02:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.705081939697266, Poisson: -0.08457158505916595  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 357/765 \u001b[2m0:02:15 • 0:02:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 358/765 \u001b[2m0:02:15 • 0:02:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 358/765 \u001b[2m0:02:15 • 0:02:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.142526626586914, Poisson: -0.10206323862075806  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 358/765 \u001b[2m0:02:15 • 0:02:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 359/765 \u001b[2m0:02:15 • 0:02:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 359/765 \u001b[2m0:02:16 • 0:02:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.630239486694336, Poisson: -0.09910961240530014  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 359/765 \u001b[2m0:02:16 • 0:02:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 360/765 \u001b[2m0:02:16 • 0:02:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 360/765 \u001b[2m0:02:16 • 0:02:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.587142944335938, Poisson: -0.09911051392555237  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 360/765 \u001b[2m0:02:16 • 0:02:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 361/765 \u001b[2m0:02:16 • 0:02:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 361/765 \u001b[2m0:02:16 • 0:02:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.15178871154785, Poisson: -0.08181598037481308   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 361/765 \u001b[2m0:02:16 • 0:02:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 362/765 \u001b[2m0:02:17 • 0:02:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 362/765 \u001b[2m0:02:17 • 0:02:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.31073570251465, Poisson: -0.08757108449935913   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 362/765 \u001b[2m0:02:17 • 0:02:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 363/765 \u001b[2m0:02:17 • 0:02:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 363/765 \u001b[2m0:02:17 • 0:02:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.43051528930664, Poisson: -0.09329986572265625   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 363/765 \u001b[2m0:02:17 • 0:02:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 364/765 \u001b[2m0:02:17 • 0:02:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 364/765 \u001b[2m0:02:17 • 0:02:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.779462814331055, Poisson: -0.10483027994632721  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 364/765 \u001b[2m0:02:17 • 0:02:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 365/765 \u001b[2m0:02:18 • 0:02:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 365/765 \u001b[2m0:02:18 • 0:02:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.157270431518555, Poisson: -0.08184707164764404  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 365/765 \u001b[2m0:02:18 • 0:02:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 366/765 \u001b[2m0:02:18 • 0:02:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 366/765 \u001b[2m0:02:18 • 0:02:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.036109924316406, Poisson: -0.09619291126728058  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 366/765 \u001b[2m0:02:18 • 0:02:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 367/765 \u001b[2m0:02:18 • 0:02:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 367/765 \u001b[2m0:02:19 • 0:02:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.9183349609375, Poisson: -0.1107921376824379     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 367/765 \u001b[2m0:02:19 • 0:02:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 368/765 \u001b[2m0:02:19 • 0:02:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 368/765 \u001b[2m0:02:19 • 0:02:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.334716796875, Poisson: -0.1079147532582283      \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 368/765 \u001b[2m0:02:19 • 0:02:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 369/765 \u001b[2m0:02:19 • 0:02:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 369/765 \u001b[2m0:02:19 • 0:02:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.446481704711914, Poisson: -0.1135539636015892   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 369/765 \u001b[2m0:02:19 • 0:02:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 370/765 \u001b[2m0:02:20 • 0:02:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 370/765 \u001b[2m0:02:20 • 0:02:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.0323486328125, Poisson: -0.09614170342683792    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 370/765 \u001b[2m0:02:20 • 0:02:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 371/765 \u001b[2m0:02:20 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 371/765 \u001b[2m0:02:20 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.340761184692383, Poisson: -0.08747261762619019  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 371/765 \u001b[2m0:02:20 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 372/765 \u001b[2m0:02:20 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 372/765 \u001b[2m0:02:21 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.18589973449707, Poisson: -0.10209566354751587   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 372/765 \u001b[2m0:02:21 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 373/765 \u001b[2m0:02:21 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 373/765 \u001b[2m0:02:21 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.515777587890625, Poisson: -0.09298166632652283  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 373/765 \u001b[2m0:02:21 • 0:02:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 374/765 \u001b[2m0:02:21 • 0:02:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 374/765 \u001b[2m0:02:21 • 0:02:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.43661117553711, Poisson: -0.11361723393201828   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 374/765 \u001b[2m0:02:21 • 0:02:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 375/765 \u001b[2m0:02:22 • 0:02:29\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 375/765 \u001b[2m0:02:22 • 0:02:29\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.196828842163086, Poisson: -0.1020895317196846   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 375/765 \u001b[2m0:02:22 • 0:02:29\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 376/765 \u001b[2m0:02:22 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 376/765 \u001b[2m0:02:22 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.19054412841797, Poisson: -0.10188775509595871   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 376/765 \u001b[2m0:02:22 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 377/765 \u001b[2m0:02:22 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 377/765 \u001b[2m0:02:22 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.329313278198242, Poisson: -0.0875338539481163   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 377/765 \u001b[2m0:02:22 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 378/765 \u001b[2m0:02:23 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 378/765 \u001b[2m0:02:23 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.348474502563477, Poisson: -0.10763359069824219  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 378/765 \u001b[2m0:02:23 • 0:02:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 379/765 \u001b[2m0:02:23 • 0:02:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 379/765 \u001b[2m0:02:23 • 0:02:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.170522689819336, Poisson: -0.10199009627103806  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 379/765 \u001b[2m0:02:23 • 0:02:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 380/765 \u001b[2m0:02:24 • 0:02:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 380/765 \u001b[2m0:02:24 • 0:02:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.17617416381836, Poisson: -0.10199982672929764   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 380/765 \u001b[2m0:02:24 • 0:02:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 381/765 \u001b[2m0:02:24 • 0:02:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 381/765 \u001b[2m0:02:24 • 0:02:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.491926193237305, Poisson: -0.11353589594364166  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 381/765 \u001b[2m0:02:24 • 0:02:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 382/765 \u001b[2m0:02:24 • 0:02:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 382/765 \u001b[2m0:02:24 • 0:02:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.7403564453125, Poisson: -0.10475480556488037    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 382/765 \u001b[2m0:02:24 • 0:02:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 383/765 \u001b[2m0:02:24 • 0:02:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 383/765 \u001b[2m0:02:25 • 0:02:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.180946350097656, Poisson: -0.1020495668053627   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 383/765 \u001b[2m0:02:25 • 0:02:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 384/765 \u001b[2m0:02:25 • 0:02:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 384/765 \u001b[2m0:02:25 • 0:02:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.01126480102539, Poisson: -0.11644819378852844   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 384/765 \u001b[2m0:02:25 • 0:02:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 385/765 \u001b[2m0:02:25 • 0:02:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 385/765 \u001b[2m0:02:25 • 0:02:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.028400421142578, Poisson: -0.11659689992666245  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 385/765 \u001b[2m0:02:25 • 0:02:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 386/765 \u001b[2m0:02:26 • 0:02:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 386/765 \u001b[2m0:02:26 • 0:02:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.86520004272461, Poisson: -0.09046062082052231   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 386/765 \u001b[2m0:02:26 • 0:02:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 387/765 \u001b[2m0:02:26 • 0:02:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 387/765 \u001b[2m0:02:26 • 0:02:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.595144271850586, Poisson: -0.09904409199953079  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 387/765 \u001b[2m0:02:26 • 0:02:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 388/765 \u001b[2m0:02:26 • 0:02:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 388/765 \u001b[2m0:02:27 • 0:02:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.583744049072266, Poisson: -0.09913177043199539  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 388/765 \u001b[2m0:02:27 • 0:02:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 389/765 \u001b[2m0:02:27 • 0:02:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 389/765 \u001b[2m0:02:27 • 0:02:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.51021385192871, Poisson: -0.09287219494581223   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 389/765 \u001b[2m0:02:27 • 0:02:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 390/765 \u001b[2m0:02:27 • 0:02:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 390/765 \u001b[2m0:02:27 • 0:02:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.901424407958984, Poisson: -0.09024031460285187  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 390/765 \u001b[2m0:02:27 • 0:02:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 391/765 \u001b[2m0:02:27 • 0:02:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 391/765 \u001b[2m0:02:28 • 0:02:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.605199813842773, Poisson: -0.078868068754673    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 391/765 \u001b[2m0:02:28 • 0:02:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 392/765 \u001b[2m0:02:28 • 0:02:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 392/765 \u001b[2m0:02:28 • 0:02:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.89341163635254, Poisson: -0.11052878201007843   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 392/765 \u001b[2m0:02:28 • 0:02:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 393/765 \u001b[2m0:02:28 • 0:02:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 393/765 \u001b[2m0:02:28 • 0:02:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.023096084594727, Poisson: -0.09612633287906647  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 393/765 \u001b[2m0:02:28 • 0:02:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 394/765 \u001b[2m0:02:29 • 0:02:19\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 394/765 \u001b[2m0:02:29 • 0:02:19\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.914955139160156, Poisson: -0.0905374139547348   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 394/765 \u001b[2m0:02:29 • 0:02:19\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 395/765 \u001b[2m0:02:29 • 0:02:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 395/765 \u001b[2m0:02:29 • 0:02:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.753278732299805, Poisson: -0.10477099567651749  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 395/765 \u001b[2m0:02:29 • 0:02:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 396/765 \u001b[2m0:02:29 • 0:02:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 396/765 \u001b[2m0:02:30 • 0:02:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.797122955322266, Poisson: -0.10499753057956696  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 396/765 \u001b[2m0:02:30 • 0:02:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 397/765 \u001b[2m0:02:30 • 0:02:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 397/765 \u001b[2m0:02:30 • 0:02:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.025386810302734, Poisson: -0.09621602296829224  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 397/765 \u001b[2m0:02:30 • 0:02:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 398/765 \u001b[2m0:02:30 • 0:02:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 398/765 \u001b[2m0:02:30 • 0:02:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.629398345947266, Poisson: -0.0991237610578537   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 398/765 \u001b[2m0:02:30 • 0:02:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 399/765 \u001b[2m0:02:30 • 0:02:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 399/765 \u001b[2m0:02:31 • 0:02:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.49061393737793, Poisson: -0.09332890063524246   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 399/765 \u001b[2m0:02:31 • 0:02:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 400/765 \u001b[2m0:02:31 • 0:02:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 400/765 \u001b[2m0:02:31 • 0:02:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.752452850341797, Poisson: -0.10496696829795837  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 400/765 \u001b[2m0:02:31 • 0:02:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 401/765 \u001b[2m0:02:31 • 0:02:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 401/765 \u001b[2m0:02:31 • 0:02:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.50249671936035, Poisson: -0.09325530380010605   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 401/765 \u001b[2m0:02:31 • 0:02:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 402/765 \u001b[2m0:02:32 • 0:02:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 402/765 \u001b[2m0:02:32 • 0:02:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.604700088500977, Poisson: -0.09890188276767731  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 402/765 \u001b[2m0:02:32 • 0:02:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 403/765 \u001b[2m0:02:32 • 0:02:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 403/765 \u001b[2m0:02:32 • 0:02:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.738080978393555, Poisson: -0.10502162575721741  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 403/765 \u001b[2m0:02:32 • 0:02:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 404/765 \u001b[2m0:02:32 • 0:02:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 404/765 \u001b[2m0:02:33 • 0:02:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.133939743041992, Poisson: -0.10192195326089859  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 404/765 \u001b[2m0:02:33 • 0:02:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 405/765 \u001b[2m0:02:33 • 0:02:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 405/765 \u001b[2m0:02:33 • 0:02:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.71113395690918, Poisson: -0.10476451367139816   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 405/765 \u001b[2m0:02:33 • 0:02:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 406/765 \u001b[2m0:02:33 • 0:02:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 406/765 \u001b[2m0:02:33 • 0:02:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.028079986572266, Poisson: -0.09624169021844864  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m 406/765 \u001b[2m0:02:33 • 0:02:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 407/765 \u001b[2m0:02:33 • 0:02:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 407/765 \u001b[2m0:02:34 • 0:02:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.779569625854492, Poisson: -0.10484415292739868  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 407/765 \u001b[2m0:02:34 • 0:02:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 408/765 \u001b[2m0:02:34 • 0:02:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 408/765 \u001b[2m0:02:34 • 0:02:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.510900497436523, Poisson: -0.11362670361995697  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 408/765 \u001b[2m0:02:34 • 0:02:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 409/765 \u001b[2m0:02:34 • 0:02:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 409/765 \u001b[2m0:02:34 • 0:02:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.517406463623047, Poisson: -0.09335844218730927  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 409/765 \u001b[2m0:02:34 • 0:02:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 410/765 \u001b[2m0:02:35 • 0:02:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 410/765 \u001b[2m0:02:35 • 0:02:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.101490020751953, Poisson: -0.09623779356479645  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 410/765 \u001b[2m0:02:35 • 0:02:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 411/765 \u001b[2m0:02:35 • 0:02:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 411/765 \u001b[2m0:02:35 • 0:02:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.498416900634766, Poisson: -0.11357088387012482  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 411/765 \u001b[2m0:02:35 • 0:02:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 412/765 \u001b[2m0:02:35 • 0:02:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 412/765 \u001b[2m0:02:36 • 0:02:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.30902099609375, Poisson: -0.10769128054380417   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 412/765 \u001b[2m0:02:36 • 0:02:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 413/765 \u001b[2m0:02:36 • 0:02:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 413/765 \u001b[2m0:02:36 • 0:02:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.43165397644043, Poisson: -0.11358172446489334   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 413/765 \u001b[2m0:02:36 • 0:02:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 414/765 \u001b[2m0:02:36 • 0:02:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 414/765 \u001b[2m0:02:36 • 0:02:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.296955108642578, Poisson: -0.10770133882761002  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 414/765 \u001b[2m0:02:36 • 0:02:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 415/765 \u001b[2m0:02:37 • 0:02:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 415/765 \u001b[2m0:02:37 • 0:02:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.49048614501953, Poisson: -0.0932009145617485    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 415/765 \u001b[2m0:02:37 • 0:02:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 416/765 \u001b[2m0:02:37 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 416/765 \u001b[2m0:02:37 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.624670028686523, Poisson: -0.099009670317173    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 416/765 \u001b[2m0:02:37 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 417/765 \u001b[2m0:02:37 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 417/765 \u001b[2m0:02:38 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.200191497802734, Poisson: -0.10194702446460724  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 417/765 \u001b[2m0:02:38 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 418/765 \u001b[2m0:02:38 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 418/765 \u001b[2m0:02:38 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.691043853759766, Poisson: -0.10477859526872635  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 418/765 \u001b[2m0:02:38 • 0:02:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 419/765 \u001b[2m0:02:38 • 0:02:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 419/765 \u001b[2m0:02:38 • 0:02:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.34872055053711, Poisson: -0.10794612765312195   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 419/765 \u001b[2m0:02:38 • 0:02:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 420/765 \u001b[2m0:02:39 • 0:02:12\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 420/765 \u001b[2m0:02:39 • 0:02:12\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.317861557006836, Poisson: -0.1077045351266861   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 420/765 \u001b[2m0:02:39 • 0:02:12\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 421/765 \u001b[2m0:02:39 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 421/765 \u001b[2m0:02:39 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.25823974609375, Poisson: -0.08748886734247208   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 421/765 \u001b[2m0:02:39 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 422/765 \u001b[2m0:02:39 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 422/765 \u001b[2m0:02:39 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.6977481842041, Poisson: -0.08458982408046722    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 422/765 \u001b[2m0:02:39 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 423/765 \u001b[2m0:02:40 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 423/765 \u001b[2m0:02:40 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.1413516998291, Poisson: -0.10186492651700974    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 423/765 \u001b[2m0:02:40 • 0:02:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 424/765 \u001b[2m0:02:40 • 0:02:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 424/765 \u001b[2m0:02:40 • 0:02:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.381986618041992, Poisson: -0.10777547210454941  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 424/765 \u001b[2m0:02:40 • 0:02:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 425/765 \u001b[2m0:02:41 • 0:02:10\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 425/765 \u001b[2m0:02:41 • 0:02:10\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.44879150390625, Poisson: -0.093277707695961     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 425/765 \u001b[2m0:02:41 • 0:02:10\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 426/765 \u001b[2m0:02:41 • 0:02:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 426/765 \u001b[2m0:02:41 • 0:02:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.77572250366211, Poisson: -0.10443708300590515   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 426/765 \u001b[2m0:02:41 • 0:02:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 427/765 \u001b[2m0:02:41 • 0:02:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 427/765 \u001b[2m0:02:41 • 0:02:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.4263916015625, Poisson: -0.0933665782213211     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 427/765 \u001b[2m0:02:41 • 0:02:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 428/765 \u001b[2m0:02:41 • 0:02:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 428/765 \u001b[2m0:02:42 • 0:02:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.447946548461914, Poisson: -0.09341086447238922  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 428/765 \u001b[2m0:02:42 • 0:02:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 429/765 \u001b[2m0:02:42 • 0:02:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 429/765 \u001b[2m0:02:42 • 0:02:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.020084381103516, Poisson: -0.09622776508331299  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 429/765 \u001b[2m0:02:42 • 0:02:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 430/765 \u001b[2m0:02:42 • 0:02:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 430/765 \u001b[2m0:02:42 • 0:02:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.167354583740234, Poisson: -0.10191415250301361  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 430/765 \u001b[2m0:02:42 • 0:02:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 431/765 \u001b[2m0:02:43 • 0:02:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 431/765 \u001b[2m0:02:43 • 0:02:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.191347122192383, Poisson: -0.102015919983387    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 431/765 \u001b[2m0:02:43 • 0:02:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 432/765 \u001b[2m0:02:43 • 0:02:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 432/765 \u001b[2m0:02:43 • 0:02:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.91743278503418, Poisson: -0.09042488783597946   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 432/765 \u001b[2m0:02:43 • 0:02:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 433/765 \u001b[2m0:02:43 • 0:02:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 433/765 \u001b[2m0:02:44 • 0:02:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.179880142211914, Poisson: -0.10197600722312927  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 433/765 \u001b[2m0:02:44 • 0:02:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 434/765 \u001b[2m0:02:44 • 0:02:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 434/765 \u001b[2m0:02:44 • 0:02:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.143842697143555, Poisson: -0.10198211669921875  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 434/765 \u001b[2m0:02:44 • 0:02:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 435/765 \u001b[2m0:02:44 • 0:02:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 435/765 \u001b[2m0:02:44 • 0:02:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.579952239990234, Poisson: -0.09911014139652252  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 435/765 \u001b[2m0:02:44 • 0:02:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 436/765 \u001b[2m0:02:44 • 0:02:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 436/765 \u001b[2m0:02:45 • 0:02:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.04136848449707, Poisson: -0.11620499938726425   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 436/765 \u001b[2m0:02:45 • 0:02:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 437/765 \u001b[2m0:02:45 • 0:02:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 437/765 \u001b[2m0:02:45 • 0:02:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.894412994384766, Poisson: -0.09048960357904434  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 437/765 \u001b[2m0:02:45 • 0:02:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 438/765 \u001b[2m0:02:45 • 0:02:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 438/765 \u001b[2m0:02:45 • 0:02:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.77016830444336, Poisson: -0.10475996136665344   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 438/765 \u001b[2m0:02:45 • 0:02:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 439/765 \u001b[2m0:02:46 • 0:02:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 439/765 \u001b[2m0:02:46 • 0:02:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.870630264282227, Poisson: -0.11065834760665894  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 439/765 \u001b[2m0:02:46 • 0:02:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 440/765 \u001b[2m0:02:46 • 0:02:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 440/765 \u001b[2m0:02:46 • 0:02:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.002723693847656, Poisson: -0.09623454511165619  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 440/765 \u001b[2m0:02:46 • 0:02:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 441/765 \u001b[2m0:02:46 • 0:02:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 441/765 \u001b[2m0:02:47 • 0:02:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.029508590698242, Poisson: -0.09621615707874298  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 441/765 \u001b[2m0:02:47 • 0:02:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 442/765 \u001b[2m0:02:47 • 0:02:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 442/765 \u001b[2m0:02:47 • 0:02:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.450267791748047, Poisson: -0.11369389295578003  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 442/765 \u001b[2m0:02:47 • 0:02:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 443/765 \u001b[2m0:02:47 • 0:02:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 443/765 \u001b[2m0:02:47 • 0:02:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.349376678466797, Poisson: -0.10773292183876038  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 443/765 \u001b[2m0:02:47 • 0:02:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 444/765 \u001b[2m0:02:47 • 0:02:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 444/765 \u001b[2m0:02:48 • 0:02:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.91932487487793, Poisson: -0.1106695756316185    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 444/765 \u001b[2m0:02:48 • 0:02:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 445/765 \u001b[2m0:02:48 • 0:02:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 445/765 \u001b[2m0:02:48 • 0:02:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.374027252197266, Poisson: -0.10787690430879593  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 445/765 \u001b[2m0:02:48 • 0:02:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 446/765 \u001b[2m0:02:48 • 0:02:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 446/765 \u001b[2m0:02:48 • 0:02:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.60036849975586, Poisson: -0.09910671412944794   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 446/765 \u001b[2m0:02:48 • 0:02:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 447/765 \u001b[2m0:02:49 • 0:02:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 447/765 \u001b[2m0:02:49 • 0:02:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.045352935791016, Poisson: -0.09619878232479095  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 447/765 \u001b[2m0:02:49 • 0:02:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 448/765 \u001b[2m0:02:49 • 0:02:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 448/765 \u001b[2m0:02:49 • 0:02:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.02604866027832, Poisson: -0.09621386975049973   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 448/765 \u001b[2m0:02:49 • 0:02:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 449/765 \u001b[2m0:02:49 • 0:01:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 449/765 \u001b[2m0:02:50 • 0:01:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.609086990356445, Poisson: -0.09849385172128677  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 449/765 \u001b[2m0:02:50 • 0:01:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 450/765 \u001b[2m0:02:50 • 0:02:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 450/765 \u001b[2m0:02:50 • 0:02:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.76195526123047, Poisson: -0.10509365051984787   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 450/765 \u001b[2m0:02:50 • 0:02:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 451/765 \u001b[2m0:02:50 • 0:01:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 451/765 \u001b[2m0:02:50 • 0:01:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.613893508911133, Poisson: -0.09910948574542999  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 451/765 \u001b[2m0:02:50 • 0:01:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 452/765 \u001b[2m0:02:50 • 0:01:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 452/765 \u001b[2m0:02:51 • 0:01:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.348949432373047, Poisson: -0.10777819901704788  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 452/765 \u001b[2m0:02:51 • 0:01:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 453/765 \u001b[2m0:02:51 • 0:01:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 453/765 \u001b[2m0:02:51 • 0:01:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.760351181030273, Poisson: -0.10495917499065399  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 453/765 \u001b[2m0:02:51 • 0:01:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 454/765 \u001b[2m0:02:51 • 0:01:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 454/765 \u001b[2m0:02:51 • 0:01:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.045129776000977, Poisson: -0.09625042229890823  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m 454/765 \u001b[2m0:02:51 • 0:01:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 455/765 \u001b[2m0:02:52 • 0:01:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 455/765 \u001b[2m0:02:52 • 0:01:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.73660659790039, Poisson: -0.1048571839928627    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 455/765 \u001b[2m0:02:52 • 0:01:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 456/765 \u001b[2m0:02:52 • 0:01:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 456/765 \u001b[2m0:02:52 • 0:01:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.018701553344727, Poisson: -0.09636573493480682  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 456/765 \u001b[2m0:02:52 • 0:01:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 457/765 \u001b[2m0:02:52 • 0:01:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 457/765 \u001b[2m0:02:53 • 0:01:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.305349349975586, Poisson: -0.1076892539858818   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 457/765 \u001b[2m0:02:53 • 0:01:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 458/765 \u001b[2m0:02:53 • 0:01:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 458/765 \u001b[2m0:02:53 • 0:01:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.944061279296875, Poisson: -0.09046798199415207  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 458/765 \u001b[2m0:02:53 • 0:01:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 459/765 \u001b[2m0:02:53 • 0:01:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 459/765 \u001b[2m0:02:53 • 0:01:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.339040756225586, Poisson: -0.10780294984579086  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 459/765 \u001b[2m0:02:53 • 0:01:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 460/765 \u001b[2m0:02:54 • 0:01:57\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 460/765 \u001b[2m0:02:54 • 0:01:57\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.622264862060547, Poisson: -0.09919681400060654  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 460/765 \u001b[2m0:02:54 • 0:01:57\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 461/765 \u001b[2m0:02:54 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 461/765 \u001b[2m0:02:54 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.863204956054688, Poisson: -0.11086340993642807  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 461/765 \u001b[2m0:02:54 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 462/765 \u001b[2m0:02:54 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 462/765 \u001b[2m0:02:55 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.18450927734375, Poisson: -0.10210908204317093   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 462/765 \u001b[2m0:02:55 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 463/765 \u001b[2m0:02:55 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 463/765 \u001b[2m0:02:55 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.0921573638916, Poisson: -0.0816550999879837     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 463/765 \u001b[2m0:02:55 • 0:01:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 464/765 \u001b[2m0:02:55 • 0:01:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 464/765 \u001b[2m0:02:55 • 0:01:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.482196807861328, Poisson: -0.11368578672409058  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 464/765 \u001b[2m0:02:55 • 0:01:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 465/765 \u001b[2m0:02:56 • 0:01:55\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 465/765 \u001b[2m0:02:56 • 0:01:55\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.71894645690918, Poisson: -0.10486404597759247   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 465/765 \u001b[2m0:02:56 • 0:01:55\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 466/765 \u001b[2m0:02:56 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 466/765 \u001b[2m0:02:56 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.761259078979492, Poisson: -0.10500288009643555  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 466/765 \u001b[2m0:02:56 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 467/765 \u001b[2m0:02:56 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 467/765 \u001b[2m0:02:56 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.789987564086914, Poisson: -0.10467837750911713  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 467/765 \u001b[2m0:02:56 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 468/765 \u001b[2m0:02:57 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 468/765 \u001b[2m0:02:57 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.94038963317871, Poisson: -0.11061599105596542   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 468/765 \u001b[2m0:02:57 • 0:01:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 469/765 \u001b[2m0:02:57 • 0:01:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 469/765 \u001b[2m0:02:57 • 0:01:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.19352912902832, Poisson: -0.10195469856262207   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 469/765 \u001b[2m0:02:57 • 0:01:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 470/765 \u001b[2m0:02:58 • 0:01:53\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 470/765 \u001b[2m0:02:58 • 0:01:53\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.622934341430664, Poisson: -0.0991571918129921   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 470/765 \u001b[2m0:02:58 • 0:01:53\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 471/765 \u001b[2m0:02:58 • 0:01:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 471/765 \u001b[2m0:02:58 • 0:01:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.145532608032227, Poisson: -0.10210911184549332  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 471/765 \u001b[2m0:02:58 • 0:01:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 472/765 \u001b[2m0:02:58 • 0:01:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 472/765 \u001b[2m0:02:58 • 0:01:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.189149856567383, Poisson: -0.10204081982374191  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 472/765 \u001b[2m0:02:58 • 0:01:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 473/765 \u001b[2m0:02:58 • 0:01:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 473/765 \u001b[2m0:02:59 • 0:01:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.60499382019043, Poisson: -0.1195843443274498    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 473/765 \u001b[2m0:02:59 • 0:01:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 474/765 \u001b[2m0:02:59 • 0:01:50\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 474/765 \u001b[2m0:02:59 • 0:01:50\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.96241569519043, Poisson: -0.11086492985486984   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 474/765 \u001b[2m0:02:59 • 0:01:50\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 475/765 \u001b[2m0:02:59 • 0:01:51\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 475/765 \u001b[2m0:02:59 • 0:01:51\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.00881576538086, Poisson: -0.09627027064561844   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 475/765 \u001b[2m0:02:59 • 0:01:51\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 476/765 \u001b[2m0:03:00 • 0:01:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 476/765 \u001b[2m0:03:00 • 0:01:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.257537841796875, Poisson: -0.10203147679567337  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 476/765 \u001b[2m0:03:00 • 0:01:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 477/765 \u001b[2m0:03:00 • 0:01:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 477/765 \u001b[2m0:03:00 • 0:01:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.521753311157227, Poisson: -0.11361809074878693  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 477/765 \u001b[2m0:03:00 • 0:01:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 478/765 \u001b[2m0:03:00 • 0:01:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 478/765 \u001b[2m0:03:01 • 0:01:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.33786964416504, Poisson: -0.10776403546333313   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 478/765 \u001b[2m0:03:01 • 0:01:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 479/765 \u001b[2m0:03:01 • 0:01:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 479/765 \u001b[2m0:03:01 • 0:01:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.839298248291016, Poisson: -0.09041623026132584  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 479/765 \u001b[2m0:03:01 • 0:01:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 480/765 \u001b[2m0:03:01 • 0:01:49\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 480/765 \u001b[2m0:03:01 • 0:01:49\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.47243881225586, Poisson: -0.11361797899007797   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 480/765 \u001b[2m0:03:01 • 0:01:49\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 481/765 \u001b[2m0:03:01 • 0:01:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 481/765 \u001b[2m0:03:02 • 0:01:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.071548461914062, Poisson: -0.09633652120828629  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 481/765 \u001b[2m0:03:02 • 0:01:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 482/765 \u001b[2m0:03:02 • 0:01:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 482/765 \u001b[2m0:03:02 • 0:01:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.10308837890625, Poisson: -0.09613187611103058   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 482/765 \u001b[2m0:03:02 • 0:01:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 483/765 \u001b[2m0:03:02 • 0:01:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 483/765 \u001b[2m0:03:02 • 0:01:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.393096923828125, Poisson: -0.0932655930519104   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 483/765 \u001b[2m0:03:02 • 0:01:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 484/765 \u001b[2m0:03:03 • 0:01:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 484/765 \u001b[2m0:03:03 • 0:01:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.508041381835938, Poisson: -0.09325002133846283  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 484/765 \u001b[2m0:03:03 • 0:01:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 485/765 \u001b[2m0:03:03 • 0:01:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 485/765 \u001b[2m0:03:03 • 0:01:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.6306095123291, Poisson: -0.09902792423963547    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 485/765 \u001b[2m0:03:03 • 0:01:47\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 486/765 \u001b[2m0:03:03 • 0:01:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 486/765 \u001b[2m0:03:04 • 0:01:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.302122116088867, Poisson: -0.08741331100463867  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 486/765 \u001b[2m0:03:04 • 0:01:46\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 487/765 \u001b[2m0:03:04 • 0:01:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 487/765 \u001b[2m0:03:04 • 0:01:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.71209144592285, Poisson: -0.08461405336856842   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 487/765 \u001b[2m0:03:04 • 0:01:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 488/765 \u001b[2m0:03:04 • 0:01:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 488/765 \u001b[2m0:03:04 • 0:01:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.88762092590332, Poisson: -0.11074787378311157   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 488/765 \u001b[2m0:03:04 • 0:01:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 489/765 \u001b[2m0:03:04 • 0:01:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 489/765 \u001b[2m0:03:05 • 0:01:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.649688720703125, Poisson: -0.09927134960889816  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 489/765 \u001b[2m0:03:05 • 0:01:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 490/765 \u001b[2m0:03:05 • 0:01:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 490/765 \u001b[2m0:03:05 • 0:01:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.90843963623047, Poisson: -0.1108170747756958    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 490/765 \u001b[2m0:03:05 • 0:01:45\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 491/765 \u001b[2m0:03:05 • 0:01:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 491/765 \u001b[2m0:03:05 • 0:01:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.3792667388916, Poisson: -0.1078619733452797     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 491/765 \u001b[2m0:03:05 • 0:01:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 492/765 \u001b[2m0:03:06 • 0:01:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 492/765 \u001b[2m0:03:06 • 0:01:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.608057022094727, Poisson: -0.09929383546113968  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 492/765 \u001b[2m0:03:06 • 0:01:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 493/765 \u001b[2m0:03:06 • 0:01:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 493/765 \u001b[2m0:03:06 • 0:01:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.761211395263672, Poisson: -0.10492335259914398  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 493/765 \u001b[2m0:03:06 • 0:01:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 494/765 \u001b[2m0:03:06 • 0:01:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 494/765 \u001b[2m0:03:07 • 0:01:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.01625633239746, Poisson: -0.09639016538858414   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 494/765 \u001b[2m0:03:07 • 0:01:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 495/765 \u001b[2m0:03:07 • 0:01:43\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 495/765 \u001b[2m0:03:07 • 0:01:43\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.058168411254883, Poisson: -0.09616664797067642  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 495/765 \u001b[2m0:03:07 • 0:01:43\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 496/765 \u001b[2m0:03:07 • 0:01:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 496/765 \u001b[2m0:03:07 • 0:01:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.605016708374023, Poisson: -0.09907364100217819  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 496/765 \u001b[2m0:03:07 • 0:01:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 497/765 \u001b[2m0:03:07 • 0:01:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 497/765 \u001b[2m0:03:08 • 0:01:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.936405181884766, Poisson: -0.11079686135053635  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 497/765 \u001b[2m0:03:08 • 0:01:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 498/765 \u001b[2m0:03:08 • 0:01:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 498/765 \u001b[2m0:03:08 • 0:01:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.78230857849121, Poisson: -0.10488864779472351   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 498/765 \u001b[2m0:03:08 • 0:01:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 499/765 \u001b[2m0:03:08 • 0:01:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 499/765 \u001b[2m0:03:08 • 0:01:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.057342529296875, Poisson: -0.11654535681009293  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 499/765 \u001b[2m0:03:08 • 0:01:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 500/765 \u001b[2m0:03:09 • 0:01:41\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 500/765 \u001b[2m0:03:09 • 0:01:41\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.325143814086914, Poisson: -0.10779325664043427  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 500/765 \u001b[2m0:03:09 • 0:01:41\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 501/765 \u001b[2m0:03:09 • 0:01:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 501/765 \u001b[2m0:03:09 • 0:01:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.733057022094727, Poisson: -0.10505848377943039  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 501/765 \u001b[2m0:03:09 • 0:01:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 502/765 \u001b[2m0:03:09 • 0:01:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 502/765 \u001b[2m0:03:10 • 0:01:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.196636199951172, Poisson: -0.08168158680200577  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m 502/765 \u001b[2m0:03:10 • 0:01:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 503/765 \u001b[2m0:03:10 • 0:01:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 503/765 \u001b[2m0:03:10 • 0:01:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90236473083496, Poisson: -0.09042704105377197   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 503/765 \u001b[2m0:03:10 • 0:01:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 504/765 \u001b[2m0:03:10 • 0:01:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 504/765 \u001b[2m0:03:10 • 0:01:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.17278289794922, Poisson: -0.10194559395313263   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 504/765 \u001b[2m0:03:10 • 0:01:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 505/765 \u001b[2m0:03:11 • 0:01:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 505/765 \u001b[2m0:03:11 • 0:01:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.057336807250977, Poisson: -0.09613290429115295  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 505/765 \u001b[2m0:03:11 • 0:01:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 506/765 \u001b[2m0:03:11 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 506/765 \u001b[2m0:03:11 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.6170654296875, Poisson: -0.0992594063282013     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 506/765 \u001b[2m0:03:11 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 507/765 \u001b[2m0:03:11 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 507/765 \u001b[2m0:03:11 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.29200553894043, Poisson: -0.10785318166017532   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 507/765 \u001b[2m0:03:11 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 508/765 \u001b[2m0:03:12 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 508/765 \u001b[2m0:03:12 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.404769897460938, Poisson: -0.0934738963842392   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 508/765 \u001b[2m0:03:12 • 0:01:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 509/765 \u001b[2m0:03:12 • 0:01:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 509/765 \u001b[2m0:03:12 • 0:01:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.445707321166992, Poisson: -0.11373444646596909  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 509/765 \u001b[2m0:03:12 • 0:01:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 510/765 \u001b[2m0:03:13 • 0:01:38\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 510/765 \u001b[2m0:03:13 • 0:01:38\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.93324851989746, Poisson: -0.09055697917938232   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 510/765 \u001b[2m0:03:13 • 0:01:38\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 511/765 \u001b[2m0:03:13 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 511/765 \u001b[2m0:03:13 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.48110008239746, Poisson: -0.09341711550951004   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 511/765 \u001b[2m0:03:13 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 512/765 \u001b[2m0:03:13 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 512/765 \u001b[2m0:03:13 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.06930923461914, Poisson: -0.09627953916788101   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 512/765 \u001b[2m0:03:13 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 513/765 \u001b[2m0:03:14 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 513/765 \u001b[2m0:03:14 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.737407684326172, Poisson: -0.08465728908777237  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 513/765 \u001b[2m0:03:14 • 0:01:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 514/765 \u001b[2m0:03:14 • 0:01:35\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 514/765 \u001b[2m0:03:14 • 0:01:35\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.765888214111328, Poisson: -0.10500418394804001  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 514/765 \u001b[2m0:03:14 • 0:01:35\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 515/765 \u001b[2m0:03:15 • 0:01:36\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 515/765 \u001b[2m0:03:15 • 0:01:36\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.21111297607422, Poisson: -0.12212420254945755   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 515/765 \u001b[2m0:03:15 • 0:01:36\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 516/765 \u001b[2m0:03:15 • 0:01:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 516/765 \u001b[2m0:03:15 • 0:01:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.060501098632812, Poisson: -0.09623979032039642  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 516/765 \u001b[2m0:03:15 • 0:01:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 517/765 \u001b[2m0:03:15 • 0:01:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 517/765 \u001b[2m0:03:15 • 0:01:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.30864143371582, Poisson: -0.08753228187561035   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 517/765 \u001b[2m0:03:15 • 0:01:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 518/765 \u001b[2m0:03:15 • 0:01:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 518/765 \u001b[2m0:03:16 • 0:01:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.776649475097656, Poisson: -0.10507254302501678  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 518/765 \u001b[2m0:03:16 • 0:01:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 519/765 \u001b[2m0:03:16 • 0:01:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 519/765 \u001b[2m0:03:16 • 0:01:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.59233856201172, Poisson: -0.09911751002073288   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 519/765 \u001b[2m0:03:16 • 0:01:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 520/765 \u001b[2m0:03:16 • 0:01:34\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 520/765 \u001b[2m0:03:16 • 0:01:34\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.406253814697266, Poisson: -0.09329336136579514  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 520/765 \u001b[2m0:03:16 • 0:01:34\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 521/765 \u001b[2m0:03:17 • 0:01:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 521/765 \u001b[2m0:03:17 • 0:01:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.895902633666992, Poisson: -0.11058278381824493  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 521/765 \u001b[2m0:03:17 • 0:01:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 522/765 \u001b[2m0:03:17 • 0:01:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 522/765 \u001b[2m0:03:17 • 0:01:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.447290420532227, Poisson: -0.09336497634649277  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 522/765 \u001b[2m0:03:17 • 0:01:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 523/765 \u001b[2m0:03:17 • 0:01:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 523/765 \u001b[2m0:03:18 • 0:01:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.702404022216797, Poisson: -0.08464144170284271  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 523/765 \u001b[2m0:03:18 • 0:01:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 524/765 \u001b[2m0:03:18 • 0:01:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 524/765 \u001b[2m0:03:18 • 0:01:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.365402221679688, Poisson: -0.1078324168920517   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 524/765 \u001b[2m0:03:18 • 0:01:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 525/765 \u001b[2m0:03:18 • 0:01:32\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 525/765 \u001b[2m0:03:18 • 0:01:32\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.91864013671875, Poisson: -0.11049604415893555   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━━\u001b[0m 525/765 \u001b[2m0:03:18 • 0:01:32\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 526/765 \u001b[2m0:03:18 • 0:01:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 526/765 \u001b[2m0:03:19 • 0:01:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.74502944946289, Poisson: -0.10491498559713364   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 526/765 \u001b[2m0:03:19 • 0:01:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 527/765 \u001b[2m0:03:19 • 0:01:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 527/765 \u001b[2m0:03:19 • 0:01:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.753398895263672, Poisson: -0.10497412830591202  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 527/765 \u001b[2m0:03:19 • 0:01:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 528/765 \u001b[2m0:03:19 • 0:01:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 528/765 \u001b[2m0:03:19 • 0:01:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.033193588256836, Poisson: -0.09612595289945602  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 528/765 \u001b[2m0:03:19 • 0:01:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 529/765 \u001b[2m0:03:20 • 0:01:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 529/765 \u001b[2m0:03:20 • 0:01:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.581336975097656, Poisson: -0.09918942302465439  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 529/765 \u001b[2m0:03:20 • 0:01:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 530/765 \u001b[2m0:03:20 • 0:01:30\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 530/765 \u001b[2m0:03:20 • 0:01:30\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.723724365234375, Poisson: -0.10498244315385818  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 530/765 \u001b[2m0:03:20 • 0:01:30\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 531/765 \u001b[2m0:03:20 • 0:01:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 531/765 \u001b[2m0:03:21 • 0:01:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.213680267333984, Poisson: -0.10206344723701477  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 531/765 \u001b[2m0:03:21 • 0:01:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 532/765 \u001b[2m0:03:21 • 0:01:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 532/765 \u001b[2m0:03:21 • 0:01:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.201120376586914, Poisson: -0.10209710896015167  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 532/765 \u001b[2m0:03:21 • 0:01:29\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 533/765 \u001b[2m0:03:21 • 0:01:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 533/765 \u001b[2m0:03:21 • 0:01:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.05521011352539, Poisson: -0.0965396910905838    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 533/765 \u001b[2m0:03:21 • 0:01:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 534/765 \u001b[2m0:03:21 • 0:01:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 534/765 \u001b[2m0:03:22 • 0:01:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.909257888793945, Poisson: -0.11082218587398529  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 534/765 \u001b[2m0:03:22 • 0:01:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 535/765 \u001b[2m0:03:22 • 0:01:28\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 535/765 \u001b[2m0:03:22 • 0:01:28\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.65955352783203, Poisson: -0.07894577085971832   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 535/765 \u001b[2m0:03:22 • 0:01:28\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 536/765 \u001b[2m0:03:22 • 0:01:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 536/765 \u001b[2m0:03:22 • 0:01:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.745389938354492, Poisson: -0.10507778078317642  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 536/765 \u001b[2m0:03:22 • 0:01:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 537/765 \u001b[2m0:03:23 • 0:01:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 537/765 \u001b[2m0:03:23 • 0:01:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.629791259765625, Poisson: -0.09936261922121048  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 537/765 \u001b[2m0:03:23 • 0:01:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 538/765 \u001b[2m0:03:23 • 0:01:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 538/765 \u001b[2m0:03:23 • 0:01:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.721967697143555, Poisson: -0.10491158068180084  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 538/765 \u001b[2m0:03:23 • 0:01:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 539/765 \u001b[2m0:03:23 • 0:01:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 539/765 \u001b[2m0:03:24 • 0:01:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.49237060546875, Poisson: -0.09336433559656143   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 539/765 \u001b[2m0:03:24 • 0:01:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 540/765 \u001b[2m0:03:24 • 0:01:26\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 540/765 \u001b[2m0:03:24 • 0:01:26\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.32317543029785, Poisson: -0.08753699064254761   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 540/765 \u001b[2m0:03:24 • 0:01:26\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 541/765 \u001b[2m0:03:24 • 0:01:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 541/765 \u001b[2m0:03:24 • 0:01:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.212947845458984, Poisson: -0.10212424397468567  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 541/765 \u001b[2m0:03:24 • 0:01:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 542/765 \u001b[2m0:03:24 • 0:01:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 542/765 \u001b[2m0:03:25 • 0:01:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.19234275817871, Poisson: -0.10211314260959625   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 542/765 \u001b[2m0:03:25 • 0:01:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 543/765 \u001b[2m0:03:25 • 0:01:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 543/765 \u001b[2m0:03:25 • 0:01:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.621742248535156, Poisson: -0.09923344105482101  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 543/765 \u001b[2m0:03:25 • 0:01:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 544/765 \u001b[2m0:03:25 • 0:01:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 544/765 \u001b[2m0:03:25 • 0:01:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.067058563232422, Poisson: -0.09618496149778366  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 544/765 \u001b[2m0:03:25 • 0:01:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 545/765 \u001b[2m0:03:26 • 0:01:24\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 545/765 \u001b[2m0:03:26 • 0:01:24\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.773244857788086, Poisson: -0.10498806834220886  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 545/765 \u001b[2m0:03:26 • 0:01:24\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 546/765 \u001b[2m0:03:26 • 0:01:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 546/765 \u001b[2m0:03:26 • 0:01:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.43944549560547, Poisson: -0.11380001902580261   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 546/765 \u001b[2m0:03:26 • 0:01:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 547/765 \u001b[2m0:03:26 • 0:01:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 547/765 \u001b[2m0:03:27 • 0:01:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.753326416015625, Poisson: -0.10509252548217773  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 547/765 \u001b[2m0:03:27 • 0:01:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 548/765 \u001b[2m0:03:27 • 0:01:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 548/765 \u001b[2m0:03:27 • 0:01:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.23395538330078, Poisson: -0.10189281404018402   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 548/765 \u001b[2m0:03:27 • 0:01:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 549/765 \u001b[2m0:03:27 • 0:01:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 549/765 \u001b[2m0:03:27 • 0:01:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.176250457763672, Poisson: -0.1021643877029419   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m 549/765 \u001b[2m0:03:27 • 0:01:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 550/765 \u001b[2m0:03:28 • 0:01:22\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 550/765 \u001b[2m0:03:28 • 0:01:22\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.32676887512207, Poisson: -0.10794815421104431   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 550/765 \u001b[2m0:03:28 • 0:01:22\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 551/765 \u001b[2m0:03:28 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 551/765 \u001b[2m0:03:28 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.617816925048828, Poisson: -0.0992942526936531   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 551/765 \u001b[2m0:03:28 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 552/765 \u001b[2m0:03:28 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 552/765 \u001b[2m0:03:28 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.32889175415039, Poisson: -0.10800859332084656   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 552/765 \u001b[2m0:03:28 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 553/765 \u001b[2m0:03:29 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 553/765 \u001b[2m0:03:29 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.16942024230957, Poisson: -0.12245308607816696   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 553/765 \u001b[2m0:03:29 • 0:01:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 554/765 \u001b[2m0:03:29 • 0:01:20\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 554/765 \u001b[2m0:03:29 • 0:01:20\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.07505989074707, Poisson: -0.09629487991333008   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 554/765 \u001b[2m0:03:29 • 0:01:20\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 555/765 \u001b[2m0:03:30 • 0:01:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 555/765 \u001b[2m0:03:30 • 0:01:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.46061897277832, Poisson: -0.09336724877357483   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 555/765 \u001b[2m0:03:30 • 0:01:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 556/765 \u001b[2m0:03:30 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 556/765 \u001b[2m0:03:30 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.70956039428711, Poisson: -0.10504449158906937   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 556/765 \u001b[2m0:03:30 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 557/765 \u001b[2m0:03:30 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 557/765 \u001b[2m0:03:30 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.05497932434082, Poisson: -0.09633395075798035   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 557/765 \u001b[2m0:03:30 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 558/765 \u001b[2m0:03:31 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 558/765 \u001b[2m0:03:31 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90784454345703, Poisson: -0.09059133380651474   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 558/765 \u001b[2m0:03:31 • 0:01:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 559/765 \u001b[2m0:03:31 • 0:01:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 559/765 \u001b[2m0:03:31 • 0:01:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.339345932006836, Poisson: -0.10778629034757614  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 559/765 \u001b[2m0:03:31 • 0:01:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 560/765 \u001b[2m0:03:32 • 0:01:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 560/765 \u001b[2m0:03:32 • 0:01:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.89039421081543, Poisson: -0.11084955930709839   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 560/765 \u001b[2m0:03:32 • 0:01:19\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 561/765 \u001b[2m0:03:32 • 0:01:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 561/765 \u001b[2m0:03:32 • 0:01:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.316572189331055, Poisson: -0.08766128122806549  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 561/765 \u001b[2m0:03:32 • 0:01:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 562/765 \u001b[2m0:03:32 • 0:01:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 562/765 \u001b[2m0:03:32 • 0:01:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.50560188293457, Poisson: -0.09355106949806213   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 562/765 \u001b[2m0:03:32 • 0:01:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 563/765 \u001b[2m0:03:32 • 0:01:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 563/765 \u001b[2m0:03:33 • 0:01:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.457378387451172, Poisson: -0.09347331523895264  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 563/765 \u001b[2m0:03:33 • 0:01:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 564/765 \u001b[2m0:03:33 • 0:01:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 564/765 \u001b[2m0:03:33 • 0:01:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.290761947631836, Poisson: -0.10799595713615417  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 564/765 \u001b[2m0:03:33 • 0:01:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 565/765 \u001b[2m0:03:33 • 0:01:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 565/765 \u001b[2m0:03:33 • 0:01:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.754764556884766, Poisson: -0.10508543252944946  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 565/765 \u001b[2m0:03:33 • 0:01:17\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 566/765 \u001b[2m0:03:34 • 0:01:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 566/765 \u001b[2m0:03:34 • 0:01:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.03472328186035, Poisson: -0.1167936846613884    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 566/765 \u001b[2m0:03:34 • 0:01:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 567/765 \u001b[2m0:03:34 • 0:01:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 567/765 \u001b[2m0:03:34 • 0:01:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.38161849975586, Poisson: -0.10793881863355637   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 567/765 \u001b[2m0:03:34 • 0:01:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 568/765 \u001b[2m0:03:34 • 0:01:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 568/765 \u001b[2m0:03:35 • 0:01:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.357261657714844, Poisson: -0.08761996775865555  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 568/765 \u001b[2m0:03:35 • 0:01:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 569/765 \u001b[2m0:03:35 • 0:01:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 569/765 \u001b[2m0:03:35 • 0:01:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.889867782592773, Poisson: -0.11075366288423538  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 569/765 \u001b[2m0:03:35 • 0:01:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 570/765 \u001b[2m0:03:35 • 0:01:15\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 570/765 \u001b[2m0:03:35 • 0:01:15\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.34160804748535, Poisson: -0.08763615787029266   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 570/765 \u001b[2m0:03:35 • 0:01:15\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 571/765 \u001b[2m0:03:35 • 0:01:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 571/765 \u001b[2m0:03:36 • 0:01:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.348304748535156, Poisson: -0.08765199780464172  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 571/765 \u001b[2m0:03:36 • 0:01:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 572/765 \u001b[2m0:03:36 • 0:01:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 572/765 \u001b[2m0:03:36 • 0:01:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.07425880432129, Poisson: -0.11665424704551697   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 572/765 \u001b[2m0:03:36 • 0:01:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 573/765 \u001b[2m0:03:36 • 0:01:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 573/765 \u001b[2m0:03:36 • 0:01:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.045265197753906, Poisson: -0.0963696613907814   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━━\u001b[0m 573/765 \u001b[2m0:03:36 • 0:01:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 574/765 \u001b[2m0:03:37 • 0:01:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 574/765 \u001b[2m0:03:37 • 0:01:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.026620864868164, Poisson: -0.09648077934980392  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 574/765 \u001b[2m0:03:37 • 0:01:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 575/765 \u001b[2m0:03:37 • 0:01:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 575/765 \u001b[2m0:03:37 • 0:01:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.9125919342041, Poisson: -0.11078745126724243    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 575/765 \u001b[2m0:03:37 • 0:01:13\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 576/765 \u001b[2m0:03:37 • 0:01:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 576/765 \u001b[2m0:03:38 • 0:01:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.444746017456055, Poisson: -0.09347876906394958  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 576/765 \u001b[2m0:03:38 • 0:01:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 577/765 \u001b[2m0:03:38 • 0:01:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 577/765 \u001b[2m0:03:38 • 0:01:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.214242935180664, Poisson: -0.1021338701248169   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 577/765 \u001b[2m0:03:38 • 0:01:12\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 578/765 \u001b[2m0:03:38 • 0:01:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 578/765 \u001b[2m0:03:38 • 0:01:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.596784591674805, Poisson: -0.09917738288640976  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 578/765 \u001b[2m0:03:38 • 0:01:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 579/765 \u001b[2m0:03:38 • 0:01:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 579/765 \u001b[2m0:03:39 • 0:01:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.591684341430664, Poisson: -0.09927138686180115  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 579/765 \u001b[2m0:03:39 • 0:01:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 580/765 \u001b[2m0:03:39 • 0:01:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 580/765 \u001b[2m0:03:39 • 0:01:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.48436737060547, Poisson: -0.1138148158788681    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 580/765 \u001b[2m0:03:39 • 0:01:11\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 581/765 \u001b[2m0:03:39 • 0:01:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 581/765 \u001b[2m0:03:39 • 0:01:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.925283432006836, Poisson: -0.09062971919775009  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 581/765 \u001b[2m0:03:39 • 0:01:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 582/765 \u001b[2m0:03:40 • 0:01:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 582/765 \u001b[2m0:03:40 • 0:01:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.90423583984375, Poisson: -0.11079645901918411   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 582/765 \u001b[2m0:03:40 • 0:01:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 583/765 \u001b[2m0:03:40 • 0:01:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 583/765 \u001b[2m0:03:40 • 0:01:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.585569381713867, Poisson: -0.07895401120185852  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 583/765 \u001b[2m0:03:40 • 0:01:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 584/765 \u001b[2m0:03:40 • 0:01:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 584/765 \u001b[2m0:03:41 • 0:01:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.48463249206543, Poisson: -0.09343615174293518   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 584/765 \u001b[2m0:03:41 • 0:01:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 585/765 \u001b[2m0:03:41 • 0:01:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 585/765 \u001b[2m0:03:41 • 0:01:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.07422637939453, Poisson: -0.09650994837284088   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 585/765 \u001b[2m0:03:41 • 0:01:09\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 586/765 \u001b[2m0:03:41 • 0:01:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 586/765 \u001b[2m0:03:41 • 0:01:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.163803100585938, Poisson: -0.10218099504709244  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 586/765 \u001b[2m0:03:41 • 0:01:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 587/765 \u001b[2m0:03:41 • 0:01:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 587/765 \u001b[2m0:03:42 • 0:01:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.735639572143555, Poisson: -0.10513748228549957  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 587/765 \u001b[2m0:03:42 • 0:01:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 588/765 \u001b[2m0:03:42 • 0:01:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 588/765 \u001b[2m0:03:42 • 0:01:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.17138671875, Poisson: -0.08189893513917923      \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 588/765 \u001b[2m0:03:42 • 0:01:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 589/765 \u001b[2m0:03:42 • 0:01:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 589/765 \u001b[2m0:03:42 • 0:01:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.80733299255371, Poisson: -0.10524075478315353   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 589/765 \u001b[2m0:03:42 • 0:01:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 590/765 \u001b[2m0:03:43 • 0:01:07\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 590/765 \u001b[2m0:03:43 • 0:01:07\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.762784957885742, Poisson: -0.10519378632307053  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 590/765 \u001b[2m0:03:43 • 0:01:07\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 591/765 \u001b[2m0:03:43 • 0:01:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 591/765 \u001b[2m0:03:43 • 0:01:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.879667282104492, Poisson: -0.09068293869495392  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 591/765 \u001b[2m0:03:43 • 0:01:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 592/765 \u001b[2m0:03:43 • 0:01:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 592/765 \u001b[2m0:03:44 • 0:01:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.876312255859375, Poisson: -0.1105591282248497   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 592/765 \u001b[2m0:03:44 • 0:01:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 593/765 \u001b[2m0:03:44 • 0:01:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 593/765 \u001b[2m0:03:44 • 0:01:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.145055770874023, Poisson: -0.10225469619035721  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 593/765 \u001b[2m0:03:44 • 0:01:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 594/765 \u001b[2m0:03:44 • 0:01:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 594/765 \u001b[2m0:03:44 • 0:01:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.336406707763672, Poisson: -0.08775860071182251  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 594/765 \u001b[2m0:03:44 • 0:01:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 595/765 \u001b[2m0:03:45 • 0:01:05\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 595/765 \u001b[2m0:03:45 • 0:01:05\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.894718170166016, Poisson: -0.09054592996835709  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 595/765 \u001b[2m0:03:45 • 0:01:05\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 596/765 \u001b[2m0:03:45 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 596/765 \u001b[2m0:03:45 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.762126922607422, Poisson: -0.1052001342177391   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 596/765 \u001b[2m0:03:45 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 597/765 \u001b[2m0:03:45 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 597/765 \u001b[2m0:03:45 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.604034423828125, Poisson: -0.0991934984922409   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m 597/765 \u001b[2m0:03:45 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 598/765 \u001b[2m0:03:46 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 598/765 \u001b[2m0:03:46 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.457157135009766, Poisson: -0.11398068815469742  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 598/765 \u001b[2m0:03:46 • 0:01:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 599/765 \u001b[2m0:03:46 • 0:01:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 599/765 \u001b[2m0:03:46 • 0:01:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.18018341064453, Poisson: -0.10216803103685379   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 599/765 \u001b[2m0:03:46 • 0:01:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 600/765 \u001b[2m0:03:47 • 0:01:03\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 600/765 \u001b[2m0:03:47 • 0:01:03\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.75962257385254, Poisson: -0.0849197655916214    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 600/765 \u001b[2m0:03:47 • 0:01:03\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 601/765 \u001b[2m0:03:47 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 601/765 \u001b[2m0:03:47 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.97867774963379, Poisson: -0.09634759277105331   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 601/765 \u001b[2m0:03:47 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 602/765 \u001b[2m0:03:47 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 602/765 \u001b[2m0:03:47 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.635732650756836, Poisson: -0.0992487221956253   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 602/765 \u001b[2m0:03:47 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 603/765 \u001b[2m0:03:48 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 603/765 \u001b[2m0:03:48 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.341726303100586, Poisson: -0.08779799938201904  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 603/765 \u001b[2m0:03:48 • 0:01:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 604/765 \u001b[2m0:03:48 • 0:01:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 604/765 \u001b[2m0:03:48 • 0:01:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.30746078491211, Poisson: -0.10809158533811569   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 604/765 \u001b[2m0:03:48 • 0:01:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 605/765 \u001b[2m0:03:49 • 0:01:01\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 605/765 \u001b[2m0:03:49 • 0:01:01\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.600067138671875, Poisson: -0.09929515421390533  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 605/765 \u001b[2m0:03:49 • 0:01:01\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 606/765 \u001b[2m0:03:49 • 0:01:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 606/765 \u001b[2m0:03:49 • 0:01:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.600677490234375, Poisson: -0.09937983751296997  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 606/765 \u001b[2m0:03:49 • 0:01:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 607/765 \u001b[2m0:03:49 • 0:01:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 607/765 \u001b[2m0:03:49 • 0:01:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.186702728271484, Poisson: -0.10223175585269928  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 607/765 \u001b[2m0:03:49 • 0:01:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 608/765 \u001b[2m0:03:49 • 0:01:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 608/765 \u001b[2m0:03:50 • 0:01:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.057613372802734, Poisson: -0.09645111113786697  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 608/765 \u001b[2m0:03:50 • 0:01:00\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 609/765 \u001b[2m0:03:50 • 0:00:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 609/765 \u001b[2m0:03:50 • 0:00:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.178607940673828, Poisson: -0.10222890973091125  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 609/765 \u001b[2m0:03:50 • 0:00:59\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 610/765 \u001b[2m0:03:50 • 0:00:59\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 610/765 \u001b[2m0:03:50 • 0:00:59\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.417633056640625, Poisson: -0.09356123208999634  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 610/765 \u001b[2m0:03:50 • 0:00:59\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 611/765 \u001b[2m0:03:51 • 0:00:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 611/765 \u001b[2m0:03:51 • 0:00:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.465253829956055, Poisson: -0.09362167865037918  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 611/765 \u001b[2m0:03:51 • 0:00:59\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 612/765 \u001b[2m0:03:51 • 0:00:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 612/765 \u001b[2m0:03:51 • 0:00:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.14314842224121, Poisson: -0.10220169275999069   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 612/765 \u001b[2m0:03:51 • 0:00:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 613/765 \u001b[2m0:03:51 • 0:00:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 613/765 \u001b[2m0:03:52 • 0:00:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.75727653503418, Poisson: -0.10522109270095825   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 613/765 \u001b[2m0:03:52 • 0:00:58\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 614/765 \u001b[2m0:03:52 • 0:00:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 614/765 \u001b[2m0:03:52 • 0:00:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.463502883911133, Poisson: -0.09337755292654037  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 614/765 \u001b[2m0:03:52 • 0:00:57\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 615/765 \u001b[2m0:03:52 • 0:00:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 615/765 \u001b[2m0:03:52 • 0:00:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.48392105102539, Poisson: -0.11386732012033463   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 615/765 \u001b[2m0:03:52 • 0:00:58\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 616/765 \u001b[2m0:03:52 • 0:00:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 616/765 \u001b[2m0:03:53 • 0:00:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.81108283996582, Poisson: -0.1052028015255928    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 616/765 \u001b[2m0:03:53 • 0:00:57\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 617/765 \u001b[2m0:03:53 • 0:00:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 617/765 \u001b[2m0:03:53 • 0:00:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.304521560668945, Poisson: -0.10814462602138519  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 617/765 \u001b[2m0:03:53 • 0:00:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 618/765 \u001b[2m0:03:53 • 0:00:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 618/765 \u001b[2m0:03:53 • 0:00:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.76158332824707, Poisson: -0.10527142137289047   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 618/765 \u001b[2m0:03:53 • 0:00:56\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 619/765 \u001b[2m0:03:54 • 0:00:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 619/765 \u001b[2m0:03:54 • 0:00:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.021570205688477, Poisson: -0.09640658646821976  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 619/765 \u001b[2m0:03:54 • 0:00:55\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 620/765 \u001b[2m0:03:54 • 0:00:56\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 620/765 \u001b[2m0:03:54 • 0:00:56\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.759620666503906, Poisson: -0.1051204577088356   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 620/765 \u001b[2m0:03:54 • 0:00:56\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 621/765 \u001b[2m0:03:54 • 0:00:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 621/765 \u001b[2m0:03:55 • 0:00:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.053144454956055, Poisson: -0.11680354923009872  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━━\u001b[0m 621/765 \u001b[2m0:03:55 • 0:00:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 622/765 \u001b[2m0:03:55 • 0:00:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 622/765 \u001b[2m0:03:55 • 0:00:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.872913360595703, Poisson: -0.09055541455745697  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 622/765 \u001b[2m0:03:55 • 0:00:55\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 623/765 \u001b[2m0:03:55 • 0:00:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 623/765 \u001b[2m0:03:55 • 0:00:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.21530532836914, Poisson: -0.08183786273002625   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 623/765 \u001b[2m0:03:55 • 0:00:54\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 624/765 \u001b[2m0:03:55 • 0:00:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 624/765 \u001b[2m0:03:56 • 0:00:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.60542869567871, Poisson: -0.09924857318401337   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 624/765 \u001b[2m0:03:56 • 0:00:53\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 625/765 \u001b[2m0:03:56 • 0:00:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 625/765 \u001b[2m0:03:56 • 0:00:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.17652702331543, Poisson: -0.10200268030166626   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 625/765 \u001b[2m0:03:56 • 0:00:54\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 626/765 \u001b[2m0:03:56 • 0:00:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 626/765 \u001b[2m0:03:56 • 0:00:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.145204544067383, Poisson: -0.10222691297531128  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 626/765 \u001b[2m0:03:56 • 0:00:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 627/765 \u001b[2m0:03:57 • 0:00:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 627/765 \u001b[2m0:03:57 • 0:00:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.08841323852539, Poisson: -0.09650090336799622   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 627/765 \u001b[2m0:03:57 • 0:00:53\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 628/765 \u001b[2m0:03:57 • 0:00:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 628/765 \u001b[2m0:03:57 • 0:00:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.491254806518555, Poisson: -0.09345397353172302  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 628/765 \u001b[2m0:03:57 • 0:00:52\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 629/765 \u001b[2m0:03:57 • 0:00:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 629/765 \u001b[2m0:03:58 • 0:00:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.903976440429688, Poisson: -0.09065891802310944  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 629/765 \u001b[2m0:03:58 • 0:00:51\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 630/765 \u001b[2m0:03:58 • 0:00:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 630/765 \u001b[2m0:03:58 • 0:00:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.21766471862793, Poisson: -0.10225459188222885   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 630/765 \u001b[2m0:03:58 • 0:00:52\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 631/765 \u001b[2m0:03:58 • 0:00:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 631/765 \u001b[2m0:03:58 • 0:00:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.777467727661133, Poisson: -0.10522960126399994  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 631/765 \u001b[2m0:03:58 • 0:00:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 632/765 \u001b[2m0:03:58 • 0:00:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 632/765 \u001b[2m0:03:59 • 0:00:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.910707473754883, Poisson: -0.11098692566156387  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 632/765 \u001b[2m0:03:59 • 0:00:51\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 633/765 \u001b[2m0:03:59 • 0:00:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 633/765 \u001b[2m0:03:59 • 0:00:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.18645477294922, Poisson: -0.10234170407056808   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 633/765 \u001b[2m0:03:59 • 0:00:50\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 634/765 \u001b[2m0:03:59 • 0:00:50\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 634/765 \u001b[2m0:03:59 • 0:00:50\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.29248046875, Poisson: -0.08780624717473984      \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 634/765 \u001b[2m0:03:59 • 0:00:50\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 635/765 \u001b[2m0:04:00 • 0:00:50\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 635/765 \u001b[2m0:04:00 • 0:00:50\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.498287200927734, Poisson: -0.0936451330780983   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 635/765 \u001b[2m0:04:00 • 0:00:50\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 636/765 \u001b[2m0:04:00 • 0:00:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 636/765 \u001b[2m0:04:00 • 0:00:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.052989959716797, Poisson: -0.09654707461595535  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 636/765 \u001b[2m0:04:00 • 0:00:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 637/765 \u001b[2m0:04:00 • 0:00:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 637/765 \u001b[2m0:04:01 • 0:00:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.621469497680664, Poisson: -0.07875768095254898  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 637/765 \u001b[2m0:04:01 • 0:00:49\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 638/765 \u001b[2m0:04:01 • 0:00:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 638/765 \u001b[2m0:04:01 • 0:00:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.37744140625, Poisson: -0.10824853181838989      \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 638/765 \u001b[2m0:04:01 • 0:00:48\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 639/765 \u001b[2m0:04:01 • 0:00:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 639/765 \u001b[2m0:04:01 • 0:00:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.236244201660156, Poisson: -0.10224587470293045  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 639/765 \u001b[2m0:04:01 • 0:00:48\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 640/765 \u001b[2m0:04:02 • 0:00:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 640/765 \u001b[2m0:04:02 • 0:00:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.215892791748047, Poisson: -0.1023206114768982   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 640/765 \u001b[2m0:04:02 • 0:00:48\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 641/765 \u001b[2m0:04:02 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 641/765 \u001b[2m0:04:02 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.46101951599121, Poisson: -0.09360704571008682   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 641/765 \u001b[2m0:04:02 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 642/765 \u001b[2m0:04:02 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 642/765 \u001b[2m0:04:02 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.354354858398438, Poisson: -0.10818653553724289  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 642/765 \u001b[2m0:04:02 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 643/765 \u001b[2m0:04:03 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 643/765 \u001b[2m0:04:03 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.61945343017578, Poisson: -0.09929303079843521   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 643/765 \u001b[2m0:04:03 • 0:00:47\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 644/765 \u001b[2m0:04:03 • 0:00:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 644/765 \u001b[2m0:04:03 • 0:00:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.731220245361328, Poisson: -0.10509055107831955  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 644/765 \u001b[2m0:04:03 • 0:00:46\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 645/765 \u001b[2m0:04:04 • 0:00:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 645/765 \u001b[2m0:04:04 • 0:00:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.64554786682129, Poisson: -0.11977849155664444   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m 645/765 \u001b[2m0:04:04 • 0:00:46\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 646/765 \u001b[2m0:04:04 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 646/765 \u001b[2m0:04:04 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.63456916809082, Poisson: -0.09936845302581787   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 646/765 \u001b[2m0:04:04 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 647/765 \u001b[2m0:04:04 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 647/765 \u001b[2m0:04:04 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.323192596435547, Poisson: -0.10799284279346466  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 647/765 \u001b[2m0:04:04 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 648/765 \u001b[2m0:04:05 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 648/765 \u001b[2m0:04:05 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.641029357910156, Poisson: -0.09944456070661545  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 648/765 \u001b[2m0:04:05 • 0:00:45\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 649/765 \u001b[2m0:04:05 • 0:00:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 649/765 \u001b[2m0:04:05 • 0:00:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.76045799255371, Poisson: -0.08474823832511902   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 649/765 \u001b[2m0:04:05 • 0:00:44\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 650/765 \u001b[2m0:04:05 • 0:00:44\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 650/765 \u001b[2m0:04:06 • 0:00:44\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.168561935424805, Poisson: -0.10231237858533859  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 650/765 \u001b[2m0:04:06 • 0:00:44\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 651/765 \u001b[2m0:04:06 • 0:00:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 651/765 \u001b[2m0:04:06 • 0:00:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.474695205688477, Poisson: -0.0935671404004097   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 651/765 \u001b[2m0:04:06 • 0:00:44\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 652/765 \u001b[2m0:04:06 • 0:00:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 652/765 \u001b[2m0:04:06 • 0:00:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.046669006347656, Poisson: -0.11684930324554443  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 652/765 \u001b[2m0:04:06 • 0:00:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 653/765 \u001b[2m0:04:06 • 0:00:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 653/765 \u001b[2m0:04:07 • 0:00:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.46649742126465, Poisson: -0.09351129084825516   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 653/765 \u001b[2m0:04:07 • 0:00:43\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 654/765 \u001b[2m0:04:07 • 0:00:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 654/765 \u001b[2m0:04:07 • 0:00:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.374460220336914, Poisson: -0.10797885805368423  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 654/765 \u001b[2m0:04:07 • 0:00:42\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 655/765 \u001b[2m0:04:07 • 0:00:42\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 655/765 \u001b[2m0:04:07 • 0:00:42\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.30933952331543, Poisson: -0.10816391557455063   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 655/765 \u001b[2m0:04:07 • 0:00:42\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 656/765 \u001b[2m0:04:08 • 0:00:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 656/765 \u001b[2m0:04:08 • 0:00:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.89350128173828, Poisson: -0.11088588833808899   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 656/765 \u001b[2m0:04:08 • 0:00:42\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 657/765 \u001b[2m0:04:08 • 0:00:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 657/765 \u001b[2m0:04:08 • 0:00:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.87434959411621, Poisson: -0.11091834306716919   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 657/765 \u001b[2m0:04:08 • 0:00:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 658/765 \u001b[2m0:04:08 • 0:00:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 658/765 \u001b[2m0:04:09 • 0:00:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.467817306518555, Poisson: -0.09358637034893036  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 658/765 \u001b[2m0:04:09 • 0:00:41\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 659/765 \u001b[2m0:04:09 • 0:00:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 659/765 \u001b[2m0:04:09 • 0:00:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.568622589111328, Poisson: -0.09929046034812927  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 659/765 \u001b[2m0:04:09 • 0:00:40\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 660/765 \u001b[2m0:04:09 • 0:00:40\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 660/765 \u001b[2m0:04:09 • 0:00:40\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.09223747253418, Poisson: -0.09644321352243423   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 660/765 \u001b[2m0:04:09 • 0:00:40\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 661/765 \u001b[2m0:04:09 • 0:00:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 661/765 \u001b[2m0:04:10 • 0:00:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.79119300842285, Poisson: -0.10510321706533432   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 661/765 \u001b[2m0:04:10 • 0:00:40\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 662/765 \u001b[2m0:04:10 • 0:00:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 662/765 \u001b[2m0:04:10 • 0:00:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.617778778076172, Poisson: -0.09937743097543716  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 662/765 \u001b[2m0:04:10 • 0:00:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 663/765 \u001b[2m0:04:10 • 0:00:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 663/765 \u001b[2m0:04:10 • 0:00:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.09708595275879, Poisson: -0.09647353738546371   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 663/765 \u001b[2m0:04:10 • 0:00:39\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 664/765 \u001b[2m0:04:11 • 0:00:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 664/765 \u001b[2m0:04:11 • 0:00:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.599708557128906, Poisson: -0.09936793148517609  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 664/765 \u001b[2m0:04:11 • 0:00:38\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 665/765 \u001b[2m0:04:11 • 0:00:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 665/765 \u001b[2m0:04:11 • 0:00:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.215360641479492, Poisson: -0.10226874053478241  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 665/765 \u001b[2m0:04:11 • 0:00:39\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 666/765 \u001b[2m0:04:11 • 0:00:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 666/765 \u001b[2m0:04:12 • 0:00:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.320337295532227, Poisson: -0.10814706981182098  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 666/765 \u001b[2m0:04:12 • 0:00:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 667/765 \u001b[2m0:04:12 • 0:00:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 667/765 \u001b[2m0:04:12 • 0:00:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.17057991027832, Poisson: -0.08193963021039963   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 667/765 \u001b[2m0:04:12 • 0:00:38\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 668/765 \u001b[2m0:04:12 • 0:00:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 668/765 \u001b[2m0:04:12 • 0:00:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.934017181396484, Poisson: -0.11095722019672394  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 668/765 \u001b[2m0:04:12 • 0:00:37\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 669/765 \u001b[2m0:04:12 • 0:00:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 669/765 \u001b[2m0:04:13 • 0:00:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.57476043701172, Poisson: -0.09952646493911743   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━━\u001b[0m 669/765 \u001b[2m0:04:13 • 0:00:36\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 670/765 \u001b[2m0:04:13 • 0:00:37\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 670/765 \u001b[2m0:04:13 • 0:00:37\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.861793518066406, Poisson: -0.09062796831130981  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 670/765 \u001b[2m0:04:13 • 0:00:37\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 671/765 \u001b[2m0:04:13 • 0:00:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 671/765 \u001b[2m0:04:13 • 0:00:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.19710350036621, Poisson: -0.10222737491130829   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 671/765 \u001b[2m0:04:13 • 0:00:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 672/765 \u001b[2m0:04:14 • 0:00:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 672/765 \u001b[2m0:04:14 • 0:00:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.047388076782227, Poisson: -0.116849385201931    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 672/765 \u001b[2m0:04:14 • 0:00:36\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 673/765 \u001b[2m0:04:14 • 0:00:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 673/765 \u001b[2m0:04:14 • 0:00:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.310956954956055, Poisson: -0.10803357511758804  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 673/765 \u001b[2m0:04:14 • 0:00:35\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 674/765 \u001b[2m0:04:14 • 0:00:35\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 674/765 \u001b[2m0:04:15 • 0:00:35\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.6702880859375, Poisson: -0.0994841679930687     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 674/765 \u001b[2m0:04:15 • 0:00:35\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 675/765 \u001b[2m0:04:15 • 0:00:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 675/765 \u001b[2m0:04:15 • 0:00:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.171405792236328, Poisson: -0.10225208103656769  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 675/765 \u001b[2m0:04:15 • 0:00:35\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 676/765 \u001b[2m0:04:15 • 0:00:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 676/765 \u001b[2m0:04:15 • 0:00:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.157621383666992, Poisson: -0.10222533345222473  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 676/765 \u001b[2m0:04:15 • 0:00:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 677/765 \u001b[2m0:04:15 • 0:00:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 677/765 \u001b[2m0:04:16 • 0:00:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.31758689880371, Poisson: -0.08783098310232162   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 677/765 \u001b[2m0:04:16 • 0:00:34\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 678/765 \u001b[2m0:04:16 • 0:00:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 678/765 \u001b[2m0:04:16 • 0:00:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.877614974975586, Poisson: -0.11098190397024155  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 678/765 \u001b[2m0:04:16 • 0:00:33\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 679/765 \u001b[2m0:04:16 • 0:00:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 679/765 \u001b[2m0:04:16 • 0:00:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.364486694335938, Poisson: -0.10811547935009003  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 679/765 \u001b[2m0:04:16 • 0:00:33\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 680/765 \u001b[2m0:04:17 • 0:00:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 680/765 \u001b[2m0:04:17 • 0:00:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.897235870361328, Poisson: -0.11115387827157974  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 680/765 \u001b[2m0:04:17 • 0:00:33\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 681/765 \u001b[2m0:04:17 • 0:00:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 681/765 \u001b[2m0:04:17 • 0:00:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.063936233520508, Poisson: -0.09639658778905869  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 681/765 \u001b[2m0:04:17 • 0:00:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 682/765 \u001b[2m0:04:17 • 0:00:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 682/765 \u001b[2m0:04:18 • 0:00:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.314693450927734, Poisson: -0.10815198719501495  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 682/765 \u001b[2m0:04:18 • 0:00:32\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 683/765 \u001b[2m0:04:18 • 0:00:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 683/765 \u001b[2m0:04:18 • 0:00:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.172088623046875, Poisson: -0.10226781666278839  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 683/765 \u001b[2m0:04:18 • 0:00:31\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 684/765 \u001b[2m0:04:18 • 0:00:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 684/765 \u001b[2m0:04:18 • 0:00:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.923828125, Poisson: -0.11099191755056381        \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 684/765 \u001b[2m0:04:18 • 0:00:31\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 685/765 \u001b[2m0:04:19 • 0:00:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 685/765 \u001b[2m0:04:19 • 0:00:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.29939079284668, Poisson: -0.08770181983709335   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 685/765 \u001b[2m0:04:19 • 0:00:31\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 686/765 \u001b[2m0:04:19 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 686/765 \u001b[2m0:04:19 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.44343376159668, Poisson: -0.09354948252439499   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 686/765 \u001b[2m0:04:19 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 687/765 \u001b[2m0:04:19 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 687/765 \u001b[2m0:04:19 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.41974639892578, Poisson: -0.09361699223518372   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 687/765 \u001b[2m0:04:19 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 688/765 \u001b[2m0:04:20 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 688/765 \u001b[2m0:04:20 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.2255859375, Poisson: -0.10238220542669296       \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 688/765 \u001b[2m0:04:20 • 0:00:30\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 689/765 \u001b[2m0:04:20 • 0:00:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 689/765 \u001b[2m0:04:20 • 0:00:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.405868530273438, Poisson: -0.09370209276676178  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 689/765 \u001b[2m0:04:20 • 0:00:29\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 690/765 \u001b[2m0:04:21 • 0:00:29\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 690/765 \u001b[2m0:04:21 • 0:00:29\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.495914459228516, Poisson: -0.11387283354997635  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 690/765 \u001b[2m0:04:21 • 0:00:29\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 691/765 \u001b[2m0:04:21 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 691/765 \u001b[2m0:04:21 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.49168586730957, Poisson: -0.09346859157085419   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 691/765 \u001b[2m0:04:21 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 692/765 \u001b[2m0:04:21 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 692/765 \u001b[2m0:04:21 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.460121154785156, Poisson: -0.11381757259368896  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 692/765 \u001b[2m0:04:21 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 693/765 \u001b[2m0:04:21 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 693/765 \u001b[2m0:04:22 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.149991989135742, Poisson: -0.10207812488079071  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m 693/765 \u001b[2m0:04:22 • 0:00:28\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 694/765 \u001b[2m0:04:22 • 0:00:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 694/765 \u001b[2m0:04:22 • 0:00:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.754638671875, Poisson: -0.10524529963731766     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 694/765 \u001b[2m0:04:22 • 0:00:27\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 695/765 \u001b[2m0:04:22 • 0:00:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 695/765 \u001b[2m0:04:22 • 0:00:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.60492706298828, Poisson: -0.0993996113538742    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 695/765 \u001b[2m0:04:22 • 0:00:27\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 696/765 \u001b[2m0:04:23 • 0:00:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 696/765 \u001b[2m0:04:23 • 0:00:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.723373413085938, Poisson: -0.08486767113208771  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 696/765 \u001b[2m0:04:23 • 0:00:27\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 697/765 \u001b[2m0:04:23 • 0:00:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 697/765 \u001b[2m0:04:23 • 0:00:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.350200653076172, Poisson: -0.08749621361494064  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 697/765 \u001b[2m0:04:23 • 0:00:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 698/765 \u001b[2m0:04:23 • 0:00:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 698/765 \u001b[2m0:04:24 • 0:00:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.365793228149414, Poisson: -0.10829178243875504  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 698/765 \u001b[2m0:04:24 • 0:00:26\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 699/765 \u001b[2m0:04:24 • 0:00:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 699/765 \u001b[2m0:04:24 • 0:00:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 25.17094612121582, Poisson: -0.1227070763707161    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 699/765 \u001b[2m0:04:24 • 0:00:25\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 700/765 \u001b[2m0:04:24 • 0:00:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 700/765 \u001b[2m0:04:24 • 0:00:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.193735122680664, Poisson: -0.10220035165548325  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 700/765 \u001b[2m0:04:24 • 0:00:25\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 701/765 \u001b[2m0:04:25 • 0:00:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 701/765 \u001b[2m0:04:25 • 0:00:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.164569854736328, Poisson: -0.10225924104452133  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 701/765 \u001b[2m0:04:25 • 0:00:25\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 702/765 \u001b[2m0:04:25 • 0:00:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 702/765 \u001b[2m0:04:25 • 0:00:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.054508209228516, Poisson: -0.11683555692434311  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 702/765 \u001b[2m0:04:25 • 0:00:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 703/765 \u001b[2m0:04:25 • 0:00:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 703/765 \u001b[2m0:04:25 • 0:00:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.189128875732422, Poisson: -0.10236984491348267  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 703/765 \u001b[2m0:04:25 • 0:00:24\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 704/765 \u001b[2m0:04:26 • 0:00:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 704/765 \u001b[2m0:04:26 • 0:00:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.62272071838379, Poisson: -0.1197962760925293    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 704/765 \u001b[2m0:04:26 • 0:00:23\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 705/765 \u001b[2m0:04:26 • 0:00:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 705/765 \u001b[2m0:04:26 • 0:00:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.612621307373047, Poisson: -0.09933579713106155  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 705/765 \u001b[2m0:04:26 • 0:00:23\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 706/765 \u001b[2m0:04:26 • 0:00:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 706/765 \u001b[2m0:04:27 • 0:00:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.183332443237305, Poisson: -0.10227450728416443  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 706/765 \u001b[2m0:04:27 • 0:00:23\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 707/765 \u001b[2m0:04:27 • 0:00:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 707/765 \u001b[2m0:04:27 • 0:00:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.332622528076172, Poisson: -0.0877760499715805   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 707/765 \u001b[2m0:04:27 • 0:00:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 708/765 \u001b[2m0:04:27 • 0:00:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 708/765 \u001b[2m0:04:27 • 0:00:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90130615234375, Poisson: -0.09062347561120987   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 708/765 \u001b[2m0:04:27 • 0:00:22\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 709/765 \u001b[2m0:04:28 • 0:00:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 709/765 \u001b[2m0:04:28 • 0:00:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.45869255065918, Poisson: -0.09361071139574051   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 709/765 \u001b[2m0:04:28 • 0:00:21\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 710/765 \u001b[2m0:04:28 • 0:00:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 710/765 \u001b[2m0:04:28 • 0:00:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.46681022644043, Poisson: -0.09367579966783524   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 710/765 \u001b[2m0:04:28 • 0:00:21\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 711/765 \u001b[2m0:04:28 • 0:00:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 711/765 \u001b[2m0:04:29 • 0:00:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.209327697753906, Poisson: -0.10235653817653656  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 711/765 \u001b[2m0:04:29 • 0:00:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 712/765 \u001b[2m0:04:29 • 0:00:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 712/765 \u001b[2m0:04:29 • 0:00:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.72150421142578, Poisson: -0.10528989136219025   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 712/765 \u001b[2m0:04:29 • 0:00:21\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 713/765 \u001b[2m0:04:29 • 0:00:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 713/765 \u001b[2m0:04:29 • 0:00:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.02463150024414, Poisson: -0.09639422595500946   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 713/765 \u001b[2m0:04:29 • 0:00:20\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 714/765 \u001b[2m0:04:29 • 0:00:20\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 714/765 \u001b[2m0:04:30 • 0:00:20\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.598779678344727, Poisson: -0.09940971434116364  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 714/765 \u001b[2m0:04:30 • 0:00:20\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 715/765 \u001b[2m0:04:30 • 0:00:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 715/765 \u001b[2m0:04:30 • 0:00:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.459402084350586, Poisson: -0.11408095061779022  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 715/765 \u001b[2m0:04:30 • 0:00:20\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 716/765 \u001b[2m0:04:30 • 0:00:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 716/765 \u001b[2m0:04:30 • 0:00:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.925418853759766, Poisson: -0.09073163568973541  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 716/765 \u001b[2m0:04:30 • 0:00:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 717/765 \u001b[2m0:04:31 • 0:00:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 717/765 \u001b[2m0:04:31 • 0:00:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.33683967590332, Poisson: -0.08788153529167175   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m\u001b[90m━\u001b[0m 717/765 \u001b[2m0:04:31 • 0:00:19\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 718/765 \u001b[2m0:04:31 • 0:00:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 718/765 \u001b[2m0:04:31 • 0:00:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.442594528198242, Poisson: -0.11403777450323105  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 718/765 \u001b[2m0:04:31 • 0:00:18\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 719/765 \u001b[2m0:04:31 • 0:00:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 719/765 \u001b[2m0:04:32 • 0:00:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.484695434570312, Poisson: -0.09360194206237793  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 719/765 \u001b[2m0:04:32 • 0:00:18\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 720/765 \u001b[2m0:04:32 • 0:00:18\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 720/765 \u001b[2m0:04:32 • 0:00:18\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.361013412475586, Poisson: -0.10816256701946259  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 720/765 \u001b[2m0:04:32 • 0:00:18\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 721/765 \u001b[2m0:04:32 • 0:00:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 721/765 \u001b[2m0:04:32 • 0:00:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.351682662963867, Poisson: -0.08778874576091766  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 721/765 \u001b[2m0:04:32 • 0:00:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 722/765 \u001b[2m0:04:32 • 0:00:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 722/765 \u001b[2m0:04:33 • 0:00:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.681488037109375, Poisson: -0.08483627438545227  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 722/765 \u001b[2m0:04:33 • 0:00:17\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 723/765 \u001b[2m0:04:33 • 0:00:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 723/765 \u001b[2m0:04:33 • 0:00:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.4982967376709, Poisson: -0.09361381083726883    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 723/765 \u001b[2m0:04:33 • 0:00:16\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 724/765 \u001b[2m0:04:33 • 0:00:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 724/765 \u001b[2m0:04:33 • 0:00:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.935379028320312, Poisson: -0.0907587856054306   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 724/765 \u001b[2m0:04:33 • 0:00:16\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 725/765 \u001b[2m0:04:34 • 0:00:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 725/765 \u001b[2m0:04:34 • 0:00:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.744428634643555, Poisson: -0.10525137931108475  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 725/765 \u001b[2m0:04:34 • 0:00:16\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 726/765 \u001b[2m0:04:34 • 0:00:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 726/765 \u001b[2m0:04:34 • 0:00:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.919313430786133, Poisson: -0.11104781180620193  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 726/765 \u001b[2m0:04:34 • 0:00:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 727/765 \u001b[2m0:04:34 • 0:00:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 727/765 \u001b[2m0:04:35 • 0:00:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.593122482299805, Poisson: -0.09950260072946548  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 727/765 \u001b[2m0:04:35 • 0:00:15\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 728/765 \u001b[2m0:04:35 • 0:00:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 728/765 \u001b[2m0:04:35 • 0:00:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.519298553466797, Poisson: -0.0936935618519783   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 728/765 \u001b[2m0:04:35 • 0:00:14\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 729/765 \u001b[2m0:04:35 • 0:00:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 729/765 \u001b[2m0:04:35 • 0:00:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.05438995361328, Poisson: -0.09660763293504715   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 729/765 \u001b[2m0:04:35 • 0:00:14\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 730/765 \u001b[2m0:04:36 • 0:00:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 730/765 \u001b[2m0:04:36 • 0:00:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.99481964111328, Poisson: -0.09633567184209824   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 730/765 \u001b[2m0:04:36 • 0:00:14\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 731/765 \u001b[2m0:04:36 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 731/765 \u001b[2m0:04:36 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.81571388244629, Poisson: -0.09070063382387161   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 731/765 \u001b[2m0:04:36 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 732/765 \u001b[2m0:04:36 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 732/765 \u001b[2m0:04:36 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.47810935974121, Poisson: -0.09368086606264114   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 732/765 \u001b[2m0:04:36 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 733/765 \u001b[2m0:04:37 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 733/765 \u001b[2m0:04:37 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.913293838500977, Poisson: -0.11116202175617218  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 733/765 \u001b[2m0:04:37 • 0:00:13\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 734/765 \u001b[2m0:04:37 • 0:00:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 734/765 \u001b[2m0:04:37 • 0:00:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.719266891479492, Poisson: -0.08489353954792023  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 734/765 \u001b[2m0:04:37 • 0:00:12\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 735/765 \u001b[2m0:04:38 • 0:00:12\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 735/765 \u001b[2m0:04:38 • 0:00:12\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.16531753540039, Poisson: -0.10242536664009094   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 735/765 \u001b[2m0:04:38 • 0:00:12\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 736/765 \u001b[2m0:04:38 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 736/765 \u001b[2m0:04:38 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.80109405517578, Poisson: -0.08490323275327682   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 736/765 \u001b[2m0:04:38 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 737/765 \u001b[2m0:04:38 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 737/765 \u001b[2m0:04:38 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.95517349243164, Poisson: -0.09651193767786026   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 737/765 \u001b[2m0:04:38 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 738/765 \u001b[2m0:04:38 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 738/765 \u001b[2m0:04:39 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.437334060668945, Poisson: -0.09368158876895905  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 738/765 \u001b[2m0:04:39 • 0:00:11\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 739/765 \u001b[2m0:04:39 • 0:00:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 739/765 \u001b[2m0:04:39 • 0:00:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.290206909179688, Poisson: -0.08795116096735     \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 739/765 \u001b[2m0:04:39 • 0:00:10\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 740/765 \u001b[2m0:04:39 • 0:00:10\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 740/765 \u001b[2m0:04:39 • 0:00:10\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.259363174438477, Poisson: -0.08205736428499222  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 740/765 \u001b[2m0:04:39 • 0:00:10\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 741/765 \u001b[2m0:04:40 • 0:00:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 741/765 \u001b[2m0:04:40 • 0:00:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.104516983032227, Poisson: -0.096626415848732    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m 741/765 \u001b[2m0:04:40 • 0:00:10\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 742/765 \u001b[2m0:04:40 • 0:00:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 742/765 \u001b[2m0:04:40 • 0:00:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.471033096313477, Poisson: -0.09366953372955322  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 742/765 \u001b[2m0:04:40 • 0:00:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 743/765 \u001b[2m0:04:40 • 0:00:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 743/765 \u001b[2m0:04:41 • 0:00:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.08424186706543, Poisson: -0.09659167379140854   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 743/765 \u001b[2m0:04:41 • 0:00:09\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 744/765 \u001b[2m0:04:41 • 0:00:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 744/765 \u001b[2m0:04:41 • 0:00:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.916379928588867, Poisson: -0.09079255163669586  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 744/765 \u001b[2m0:04:41 • 0:00:08\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 745/765 \u001b[2m0:04:41 • 0:00:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 745/765 \u001b[2m0:04:41 • 0:00:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.71767234802246, Poisson: -0.10537280142307281   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 745/765 \u001b[2m0:04:41 • 0:00:08\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 746/765 \u001b[2m0:04:42 • 0:00:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 746/765 \u001b[2m0:04:42 • 0:00:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.61359977722168, Poisson: -0.09948355704545975   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 746/765 \u001b[2m0:04:42 • 0:00:08\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 747/765 \u001b[2m0:04:42 • 0:00:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 747/765 \u001b[2m0:04:42 • 0:00:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.49709129333496, Poisson: -0.09374634176492691   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 747/765 \u001b[2m0:04:42 • 0:00:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 748/765 \u001b[2m0:04:42 • 0:00:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 748/765 \u001b[2m0:04:42 • 0:00:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.611040115356445, Poisson: -0.09951072186231613  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 748/765 \u001b[2m0:04:42 • 0:00:07\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 749/765 \u001b[2m0:04:43 • 0:00:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 749/765 \u001b[2m0:04:43 • 0:00:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.0173282623291, Poisson: -0.09661507606506348    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 749/765 \u001b[2m0:04:43 • 0:00:06\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 750/765 \u001b[2m0:04:43 • 0:00:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 750/765 \u001b[2m0:04:43 • 0:00:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.05352783203125, Poisson: -0.09659050405025482   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 750/765 \u001b[2m0:04:43 • 0:00:06\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 751/765 \u001b[2m0:04:43 • 0:00:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 751/765 \u001b[2m0:04:44 • 0:00:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.207345962524414, Poisson: -0.10246887058019638  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 751/765 \u001b[2m0:04:44 • 0:00:06\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 752/765 \u001b[2m0:04:44 • 0:00:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 752/765 \u001b[2m0:04:44 • 0:00:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.024946212768555, Poisson: -0.09657814353704453  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 752/765 \u001b[2m0:04:44 • 0:00:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 753/765 \u001b[2m0:04:44 • 0:00:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 753/765 \u001b[2m0:04:44 • 0:00:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.75469398498535, Poisson: -0.10540928691625595   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 753/765 \u001b[2m0:04:44 • 0:00:05\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 754/765 \u001b[2m0:04:45 • 0:00:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 754/765 \u001b[2m0:04:45 • 0:00:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.90352439880371, Poisson: -0.09076559543609619   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 754/765 \u001b[2m0:04:45 • 0:00:05\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 755/765 \u001b[2m0:04:45 • 0:00:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 755/765 \u001b[2m0:04:45 • 0:00:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.797571182250977, Poisson: -0.10542497038841248  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 755/765 \u001b[2m0:04:45 • 0:00:04\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 756/765 \u001b[2m0:04:45 • 0:00:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 756/765 \u001b[2m0:04:46 • 0:00:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.466381072998047, Poisson: -0.11365482211112976  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 756/765 \u001b[2m0:04:46 • 0:00:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 757/765 \u001b[2m0:04:46 • 0:00:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 757/765 \u001b[2m0:04:46 • 0:00:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.74541664123535, Poisson: -0.10543787479400635   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 757/765 \u001b[2m0:04:46 • 0:00:04\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 758/765 \u001b[2m0:04:46 • 0:00:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 758/765 \u001b[2m0:04:46 • 0:00:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.44692611694336, Poisson: -0.09366541355848312   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 758/765 \u001b[2m0:04:46 • 0:00:03\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 759/765 \u001b[2m0:04:46 • 0:00:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 759/765 \u001b[2m0:04:47 • 0:00:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.27775764465332, Poisson: -0.10833136737346649   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 759/765 \u001b[2m0:04:47 • 0:00:03\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 760/765 \u001b[2m0:04:47 • 0:00:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 760/765 \u001b[2m0:04:47 • 0:00:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.2319278717041, Poisson: -0.10245988517999649    \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 760/765 \u001b[2m0:04:47 • 0:00:02\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 761/765 \u001b[2m0:04:47 • 0:00:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 761/765 \u001b[2m0:04:47 • 0:00:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.87559700012207, Poisson: -0.09086116403341293   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 761/765 \u001b[2m0:04:47 • 0:00:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 762/765 \u001b[2m0:04:48 • 0:00:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 762/765 \u001b[2m0:04:48 • 0:00:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.04041290283203, Poisson: -0.09660524874925613   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 762/765 \u001b[2m0:04:48 • 0:00:02\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 763/765 \u001b[2m0:04:48 • 0:00:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 763/765 \u001b[2m0:04:48 • 0:00:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.87413215637207, Poisson: -0.11119227111339569   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 763/765 \u001b[2m0:04:48 • 0:00:01\u001b[0m \u001b[2;4m2.65it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 764/765 \u001b[2m0:04:48 • 0:00:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 764/765 \u001b[2m0:04:49 • 0:00:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.96841812133789, Poisson: -0.09672267735004425   \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━\u001b[0m\u001b[35m╸\u001b[0m 764/765 \u001b[2m0:04:49 • 0:00:01\u001b[0m \u001b[2;4m2.67it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.741498947143555, Poisson: -0.08491457253694534  \u001b[0m\n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.174591064453125, Poisson: -0.08205334842205048                 \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.474199295043945, Poisson: -0.1141667366027832                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.252899169921875, Poisson: -0.08789469301700592it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.726030349731445, Poisson: -0.10541643947362921it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.71944236755371, Poisson: -0.08482178300619125\u001b[2;4m8.20it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.795509338378906, Poisson: -0.08484726399183273[2;4m8.20it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.390716552734375, Poisson: -0.10839201509952545[2;4m8.20it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.90187644958496, Poisson: -0.11125152558088303\u001b[2;4m8.20it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.61016273498535, Poisson: -0.09957005083560944\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.080995559692383, Poisson: -0.08206482976675034[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.719736099243164, Poisson: -0.10544822365045547[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.014415740966797, Poisson: -0.096670962870121 \u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.759136199951172, Poisson: -0.10554072260856628[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.011444091796875, Poisson: -0.11678408831357956[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.483537673950195, Poisson: -0.09387410432100296[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.586347579956055, Poisson: -0.09951671212911606[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.148466110229492, Poisson: -0.10255124419927597[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.64990997314453, Poisson: -0.0790940597653389 \u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.959596633911133, Poisson: -0.11126449704170227[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.02794075012207, Poisson: -0.09673741459846497\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.68817138671875, Poisson: -0.11991552263498306\u001b[2;4m8.20it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.6884765625, Poisson: -0.09962115436792374[0m \u001b[2;4m8.20it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.021608352661133, Poisson: -0.11691244691610336[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.48906135559082, Poisson: -0.09364556521177292\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.85381317138672, Poisson: -0.09063024073839188\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.078285217285156, Poisson: -0.09651201963424683[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.464078903198242, Poisson: -0.09370408952236176[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.212141036987305, Poisson: -0.10238029062747955[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.192331314086914, Poisson: -0.08203324675559998[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.624908447265625, Poisson: -0.0995558500289917\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.458314895629883, Poisson: -0.11398416757583618[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.761682510375977, Poisson: -0.10533803701400757[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.452919006347656, Poisson: -0.09377363324165344[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.804271697998047, Poisson: -0.10534661263227463[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.396343231201172, Poisson: -0.09376662224531174[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 19.458505630493164, Poisson: -0.09365436434745789[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.047607421875, Poisson: -0.11688768863677979m \u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 16.091678619384766, Poisson: -0.07625492662191391[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.93729591369629, Poisson: -0.11114904284477234\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.05024528503418, Poisson: -0.09665868431329727\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.03590202331543, Poisson: -0.11705663800239563\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.334291458129883, Poisson: -0.08788107335567474[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 15.45654296875, Poisson: -0.073356598615646360m \u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.30838394165039, Poisson: -0.10835734754800797\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.094877243041992, Poisson: -0.11712042987346649[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.912992477416992, Poisson: -0.11108077317476273[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.738901138305664, Poisson: -0.10539423674345016[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.189945220947266, Poisson: -0.10253528505563736[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.0674991607666, Poisson: -0.11695557832717896 \u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.943021774291992, Poisson: -0.09093046933412552[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.95169448852539, Poisson: -0.0904151126742363 \u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.182706832885742, Poisson: -0.1025085523724556\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.088436126708984, Poisson: -0.09663637727499008[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.75460433959961, Poisson: -0.10551492869853973\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.502819061279297, Poisson: -0.11414351314306259[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.81133270263672, Poisson: -0.10547111183404922\u001b[2;4m8.21it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.245851516723633, Poisson: -0.10239611566066742[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 18.34769630432129, Poisson: -0.08787565678358078\u001b[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.654518127441406, Poisson: -0.09947939217090607[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.579660415649414, Poisson: -0.09957797080278397[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.635194778442383, Poisson: -0.12000980973243713[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 23.437232971191406, Poisson: -0.11393661797046661[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.232690811157227, Poisson: -0.10251738131046295[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 15.982871055603027, Poisson: -0.07637123018503189[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.052274703979492, Poisson: -0.0966184213757515\u001b[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.63153076171875, Poisson: -0.09948145598173141\u001b[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.193172454833984, Poisson: -0.10253100097179413[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 22.296239852905273, Poisson: -0.1083475798368454\u001b[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.070253372192383, Poisson: -0.0966835767030716\u001b[2;4m8.22it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 17.800703048706055, Poisson: -0.08493541181087494it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 21.190921783447266, Poisson: -0.10247200727462769it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 20.6121768951416, Poisson: -0.0996500030159950322it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KMultinomial: 24.036474227905273, Poisson: -0.11712503433227539it/s\u001b[0m                  \n",
+      "Epoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_epoch:\u001b[0m\n",
+      "                                                               \u001b[3m20.649           \u001b[0m`Trainer.fit` stopped: `max_epochs=1` reached.\n",
+      "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KEpoch 0/0  \u001b[35m━━━━━━━━━━━━━━━━\u001b[0m 765/765 \u001b[2m0:04:49 • 0:00:00\u001b[0m \u001b[2;4m2.63it/s\u001b[0m \u001b[3mv_num: s659      \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_step: \u001b[0m\n",
+      "                                                               \u001b[3m19.872           \u001b[0m\n",
+      "                                                               \u001b[3mtrain_loss_epoch:\u001b[0m\n",
+      "                                                               \u001b[3m20.649           \u001b[0m\n",
+      "\u001b[?25h\u001b[1;34mwandb\u001b[0m: \n",
+      "\u001b[1;34mwandb\u001b[0m: 🚀 View run \u001b[33mfinetune_test_0\u001b[0m at: \u001b[34mhttps://genentech.wandb.io/grelu/decima/runs/g0m7s659\u001b[0m\n",
+      "\u001b[0m"
      ]
     },
     {
@@ -17809,18 +23628,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "id": "538d1250-8fc2-460b-b5fc-61ec083cca86",
-   "metadata": {
-    "collapsed": true,
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:35:16.458997Z",
-     "iopub.status.busy": "2025-11-21T22:35:16.458827Z",
-     "iopub.status.idle": "2025-11-21T22:35:16.461130Z",
-     "shell.execute_reply": "2025-11-21T22:35:16.460540Z"
-    },
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Uncomment if necessary\n",
@@ -17848,20 +23656,13 @@
    "cell_type": "code",
    "execution_count": 30,
    "id": "11e3047e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:35:16.462669Z",
-     "iopub.status.busy": "2025-11-21T22:35:16.462525Z",
-     "iopub.status.idle": "2025-11-21T22:35:16.465688Z",
-     "shell.execute_reply": "2025-11-21T22:35:16.465188Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "./example/lightning_logs/g20ya0al/checkpoints/epoch=0-step=154.ckpt\n"
+      "./example/lightning_logs/g0m7s659/checkpoints/epoch=0-step=153.ckpt\n"
      ]
     }
    ],
@@ -17874,19 +23675,12 @@
    "cell_type": "code",
    "execution_count": 31,
    "id": "2d04b6e9-373f-4cf4-80ca-662f1dc60a75",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:35:16.467043Z",
-     "iopub.status.busy": "2025-11-21T22:35:16.466885Z",
-     "iopub.status.idle": "2025-11-21T22:35:16.469956Z",
-     "shell.execute_reply": "2025-11-21T22:35:16.469456Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'./example/lightning_logs/g20ya0al/checkpoints/epoch=0-step=154.ckpt,./example/lightning_logs/g20ya0al/checkpoints/epoch=0-step=154.ckpt'"
+       "'./example/lightning_logs/g0m7s659/checkpoints/epoch=0-step=153.ckpt,./example/lightning_logs/g0m7s659/checkpoints/epoch=0-step=153.ckpt'"
       ]
      },
      "execution_count": 31,
@@ -17902,25 +23696,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "id": "5b94d98f-7889-4223-8c0a-7cec1a365a73",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:35:16.471038Z",
-     "iopub.status.busy": "2025-11-21T22:35:16.470916Z",
-     "iopub.status.idle": "2025-11-21T22:39:05.747907Z",
-     "shell.execute_reply": "2025-11-21T22:39:05.747013Z"
-    }
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
+      "decima - INFO - Using device: 0 and genome: hg38 for prediction.\n",
+      "decima - INFO - Loading model ['./example/lightning_logs/g0m7s659/checkpoints/epoch=0-step=153.ckpt', './example/lightning_logs/g0m7s659/checkpoints/epoch=0-step=153.ckpt']...\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/utilities/cloud_io.py:73: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "decima - INFO - Making predictions\n",
+      "/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/lightning_fabric/plugins/environments/slurm.py:204: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3.11 /home/lala8/.local/bin/decima predict-genes --ou ...\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 115/115 \u001b[2m0:05:16 • 0:00:00\u001b[0m \u001b[2;4m0.36it/s\u001b[0m it/s\u001b[0m it/s\u001b[0m \n",
+      "\u001b[?25h/gpfs/scratchfs01/site/u/lala8/conda/envs/decima/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n",
+      "decima - INFO - Creating anndata\n",
+      "decima - INFO - Evaluating performance\n",
+      "Performance on genes in the train dataset.\n",
+      "Mean Pearson Correlation per gene: Mean: 0.01.\n",
+      "Mean Pearson Correlation per gene using size factor (baseline): 0.03.\n",
+      "Mean Pearson Correlation per pseudobulk: -0.00\n",
+      "\n",
+      "Performance on genes in the val dataset.\n",
+      "Mean Pearson Correlation per gene: Mean: -0.02.\n",
+      "Mean Pearson Correlation per gene using size factor (baseline): 0.05.\n",
+      "Mean Pearson Correlation per pseudobulk:  0.01\n",
+      "\n",
+      "Performance on genes in the test dataset.\n",
+      "Mean Pearson Correlation per gene: Mean: -0.02.\n",
+      "Mean Pearson Correlation per gene using size factor (baseline): -0.00.\n",
+      "Mean Pearson Correlation per pseudobulk: -0.02\n",
+      "\n",
+      "\u001b[0m"
      ]
     },
     {
@@ -18954,7 +24769,7 @@
     "--metadata {ad_file_path} \\\n",
     "--device 0 \\\n",
     "--batch-size 8 \\\n",
-    "--num-workers 32 \\\n",
+    "--num-workers 16 \\\n",
     "--max_seq_shift 0 \\\n",
     "--genome hg38 \\\n",
     "--save-replicates"
@@ -18970,16 +24785,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 35,
    "id": "e7bceeb6-5e91-455e-b40f-2b1c02f90d39",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:39:05.750232Z",
-     "iopub.status.busy": "2025-11-21T22:39:05.750008Z",
-     "iopub.status.idle": "2025-11-21T22:39:05.781095Z",
-     "shell.execute_reply": "2025-11-21T22:39:05.780468Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ad_out = anndata.read_h5ad(\"example/test_preds.h5ad\")"
@@ -18987,27 +24795,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 36,
    "id": "af5c37c9-53df-439e-9501-e06eed5ae8f7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:39:05.783023Z",
-     "iopub.status.busy": "2025-11-21T22:39:05.782863Z",
-     "iopub.status.idle": "2025-11-21T22:39:05.785830Z",
-     "shell.execute_reply": "2025-11-21T22:39:05.785382Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AnnData object with n_obs × n_vars = 50 × 920\n",
+       "AnnData object with n_obs × n_vars = 50 × 919\n",
        "    obs: 'cell_type', 'tissue', 'disease', 'study', 'size_factor', 'train_pearson', 'val_pearson', 'test_pearson'\n",
        "    var: 'chrom', 'start', 'end', 'strand', 'gene_start', 'gene_end', 'gene_length', 'gene_mask_start', 'gene_mask_end', 'dataset', 'pearson', 'size_factor_pearson'\n",
        "    layers: 'preds', 'preds_finetune_test_0'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -19026,16 +24827,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "id": "c507929c-a73b-4641-b1de-9f066385a972",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:39:05.787091Z",
-     "iopub.status.busy": "2025-11-21T22:39:05.786958Z",
-     "iopub.status.idle": "2025-11-21T22:39:05.794747Z",
-     "shell.execute_reply": "2025-11-21T22:39:05.794293Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -19075,10 +24869,10 @@
        "      <td>t_0</td>\n",
        "      <td>d_0</td>\n",
        "      <td>st_0</td>\n",
-       "      <td>4946.397461</td>\n",
-       "      <td>0.010020</td>\n",
-       "      <td>0.171944</td>\n",
-       "      <td>0.122095</td>\n",
+       "      <td>4947.391113</td>\n",
+       "      <td>-0.023931</td>\n",
+       "      <td>-0.007586</td>\n",
+       "      <td>-0.125109</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pseudobulk_1</th>\n",
@@ -19086,10 +24880,10 @@
        "      <td>t_0</td>\n",
        "      <td>d_1</td>\n",
        "      <td>st_0</td>\n",
-       "      <td>4858.091797</td>\n",
-       "      <td>-0.024151</td>\n",
-       "      <td>0.061900</td>\n",
-       "      <td>-0.169406</td>\n",
+       "      <td>4851.750488</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>-0.023935</td>\n",
+       "      <td>0.067142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pseudobulk_2</th>\n",
@@ -19097,10 +24891,10 @@
        "      <td>t_0</td>\n",
        "      <td>d_2</td>\n",
        "      <td>st_1</td>\n",
-       "      <td>4921.185547</td>\n",
-       "      <td>0.007005</td>\n",
-       "      <td>-0.079252</td>\n",
-       "      <td>-0.094602</td>\n",
+       "      <td>4922.177734</td>\n",
+       "      <td>-0.011712</td>\n",
+       "      <td>0.132251</td>\n",
+       "      <td>-0.088595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pseudobulk_3</th>\n",
@@ -19108,10 +24902,10 @@
        "      <td>t_0</td>\n",
        "      <td>d_0</td>\n",
        "      <td>st_1</td>\n",
-       "      <td>4928.486816</td>\n",
-       "      <td>0.016869</td>\n",
-       "      <td>-0.023038</td>\n",
-       "      <td>0.007967</td>\n",
+       "      <td>4921.185547</td>\n",
+       "      <td>0.028251</td>\n",
+       "      <td>-0.114663</td>\n",
+       "      <td>0.034809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pseudobulk_4</th>\n",
@@ -19119,10 +24913,10 @@
        "      <td>t_0</td>\n",
        "      <td>d_1</td>\n",
        "      <td>st_2</td>\n",
-       "      <td>4756.819336</td>\n",
-       "      <td>0.050297</td>\n",
-       "      <td>0.160398</td>\n",
-       "      <td>-0.101163</td>\n",
+       "      <td>4750.456543</td>\n",
+       "      <td>-0.009447</td>\n",
+       "      <td>-0.069073</td>\n",
+       "      <td>0.072288</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -19130,21 +24924,21 @@
       ],
       "text/plain": [
        "             cell_type tissue disease study  size_factor  train_pearson  \\\n",
-       "pseudobulk_0      ct_0    t_0     d_0  st_0  4946.397461       0.010020   \n",
-       "pseudobulk_1      ct_0    t_0     d_1  st_0  4858.091797      -0.024151   \n",
-       "pseudobulk_2      ct_0    t_0     d_2  st_1  4921.185547       0.007005   \n",
-       "pseudobulk_3      ct_0    t_0     d_0  st_1  4928.486816       0.016869   \n",
-       "pseudobulk_4      ct_0    t_0     d_1  st_2  4756.819336       0.050297   \n",
+       "pseudobulk_0      ct_0    t_0     d_0  st_0  4947.391113      -0.023931   \n",
+       "pseudobulk_1      ct_0    t_0     d_1  st_0  4851.750488       0.000509   \n",
+       "pseudobulk_2      ct_0    t_0     d_2  st_1  4922.177734      -0.011712   \n",
+       "pseudobulk_3      ct_0    t_0     d_0  st_1  4921.185547       0.028251   \n",
+       "pseudobulk_4      ct_0    t_0     d_1  st_2  4750.456543      -0.009447   \n",
        "\n",
        "              val_pearson  test_pearson  \n",
-       "pseudobulk_0     0.171944      0.122095  \n",
-       "pseudobulk_1     0.061900     -0.169406  \n",
-       "pseudobulk_2    -0.079252     -0.094602  \n",
-       "pseudobulk_3    -0.023038      0.007967  \n",
-       "pseudobulk_4     0.160398     -0.101163  "
+       "pseudobulk_0    -0.007586     -0.125109  \n",
+       "pseudobulk_1    -0.023935      0.067142  \n",
+       "pseudobulk_2     0.132251     -0.088595  \n",
+       "pseudobulk_3    -0.114663      0.034809  \n",
+       "pseudobulk_4    -0.069073      0.072288  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -19155,16 +24949,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "id": "121a7787-4c74-465f-ae93-b529564cc2fa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T22:39:05.796225Z",
-     "iopub.status.busy": "2025-11-21T22:39:05.796089Z",
-     "iopub.status.idle": "2025-11-21T22:39:05.802450Z",
-     "shell.execute_reply": "2025-11-21T22:39:05.801934Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -19205,77 +24992,77 @@
        "    <tr>\n",
        "      <th>gene_0</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>26027160</td>\n",
+       "      <td>26551448</td>\n",
+       "      <td>+</td>\n",
        "      <td>26191000</td>\n",
        "      <td>26715288</td>\n",
-       "      <td>+</td>\n",
-       "      <td>26354840</td>\n",
-       "      <td>26879128</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
        "      <td>train</td>\n",
-       "      <td>0.177304</td>\n",
-       "      <td>-0.062494</td>\n",
+       "      <td>-0.206382</td>\n",
+       "      <td>-0.067028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gene_1</th>\n",
        "      <td>chr19</td>\n",
+       "      <td>41439097</td>\n",
+       "      <td>41963385</td>\n",
+       "      <td>-</td>\n",
        "      <td>41275257</td>\n",
        "      <td>41799545</td>\n",
-       "      <td>-</td>\n",
-       "      <td>41111417</td>\n",
-       "      <td>41635705</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
        "      <td>train</td>\n",
-       "      <td>0.049450</td>\n",
-       "      <td>-0.037428</td>\n",
+       "      <td>-0.001485</td>\n",
+       "      <td>-0.033103</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gene_2</th>\n",
        "      <td>chr1</td>\n",
+       "      <td>80101706</td>\n",
+       "      <td>80625994</td>\n",
+       "      <td>-</td>\n",
        "      <td>79937866</td>\n",
        "      <td>80462154</td>\n",
-       "      <td>-</td>\n",
-       "      <td>79774026</td>\n",
-       "      <td>80298314</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
        "      <td>train</td>\n",
-       "      <td>-0.095439</td>\n",
-       "      <td>0.240203</td>\n",
+       "      <td>0.052491</td>\n",
+       "      <td>0.232561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gene_4</th>\n",
        "      <td>chr16</td>\n",
+       "      <td>4069048</td>\n",
+       "      <td>4593336</td>\n",
+       "      <td>-</td>\n",
        "      <td>3905208</td>\n",
        "      <td>4429496</td>\n",
-       "      <td>-</td>\n",
-       "      <td>3741368</td>\n",
-       "      <td>4265656</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
        "      <td>train</td>\n",
-       "      <td>-0.092946</td>\n",
-       "      <td>-0.042283</td>\n",
+       "      <td>0.035831</td>\n",
+       "      <td>-0.040373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gene_5</th>\n",
        "      <td>chr10</td>\n",
+       "      <td>22331801</td>\n",
+       "      <td>22856089</td>\n",
+       "      <td>+</td>\n",
        "      <td>22495641</td>\n",
        "      <td>23019929</td>\n",
-       "      <td>+</td>\n",
-       "      <td>22659481</td>\n",
-       "      <td>23183769</td>\n",
        "      <td>524288</td>\n",
        "      <td>163840</td>\n",
        "      <td>524288</td>\n",
        "      <td>train</td>\n",
-       "      <td>-0.310151</td>\n",
-       "      <td>-0.069181</td>\n",
+       "      <td>0.368011</td>\n",
+       "      <td>-0.066814</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -19283,21 +25070,21 @@
       ],
       "text/plain": [
        "        chrom     start       end strand  gene_start  gene_end  gene_length  \\\n",
-       "gene_0   chr1  26191000  26715288      +    26354840  26879128       524288   \n",
-       "gene_1  chr19  41275257  41799545      -    41111417  41635705       524288   \n",
-       "gene_2   chr1  79937866  80462154      -    79774026  80298314       524288   \n",
-       "gene_4  chr16   3905208   4429496      -     3741368   4265656       524288   \n",
-       "gene_5  chr10  22495641  23019929      +    22659481  23183769       524288   \n",
+       "gene_0   chr1  26027160  26551448      +    26191000  26715288       524288   \n",
+       "gene_1  chr19  41439097  41963385      -    41275257  41799545       524288   \n",
+       "gene_2   chr1  80101706  80625994      -    79937866  80462154       524288   \n",
+       "gene_4  chr16   4069048   4593336      -     3905208   4429496       524288   \n",
+       "gene_5  chr10  22331801  22856089      +    22495641  23019929       524288   \n",
        "\n",
        "        gene_mask_start  gene_mask_end dataset   pearson  size_factor_pearson  \n",
-       "gene_0           163840         524288   train  0.177304            -0.062494  \n",
-       "gene_1           163840         524288   train  0.049450            -0.037428  \n",
-       "gene_2           163840         524288   train -0.095439             0.240203  \n",
-       "gene_4           163840         524288   train -0.092946            -0.042283  \n",
-       "gene_5           163840         524288   train -0.310151            -0.069181  "
+       "gene_0           163840         524288   train -0.206382            -0.067028  \n",
+       "gene_1           163840         524288   train -0.001485            -0.033103  \n",
+       "gene_2           163840         524288   train  0.052491             0.232561  \n",
+       "gene_4           163840         524288   train  0.035831            -0.040373  \n",
+       "gene_5           163840         524288   train  0.368011            -0.066814  "
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -19305,11 +25092,19 @@
    "source": [
     "ad_out.var.head()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08050a03-4be0-4b06-9a9e-6f2350bf3027",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "decima2",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -19323,7 +25118,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/4-modisco.ipynb b/docs/tutorials/4-modisco.ipynb
index fb1fe28..6448959 100644
--- a/docs/tutorials/4-modisco.ipynb
+++ b/docs/tutorials/4-modisco.ipynb
@@ -36,23 +36,12 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "2852f3c3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:07:31.329581Z",
-     "iopub.status.busy": "2025-11-21T06:07:31.329445Z",
-     "iopub.status.idle": "2025-11-21T06:08:16.524350Z",
-     "shell.execute_reply": "2025-11-21T06:08:16.523710Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:02.0 (1590.8MB/s)\n",
       "\tcell_type\ttissue\torgan\tdisease\tstudy\tdataset\tregion\tsubregion\tcelltype_coarse\tn_cells\ttotal_counts\tn_genes\tsize_factor\ttrain_pearson\tval_pearson\ttest_pearson\n",
       "agg_1112\tCGE interneuron\tAmygdala_Amygdala\tCNS\thealthy\tjhpce#tran2021\tbrain_atlas\tAmygdala\tAmygdala\t\t674\t17421456.0\t16954\t42512.927350555474\t0.9454484906394379\t0.8549755331049285\t0.8667663764982964\n",
       "agg_1113\tCGE interneuron\tAmygdala_Basolateral nuclear group (BLN) - lateral nucleus - La\tCNS\thealthy\tSCR_016152\tbrain_atlas\tAmygdala\tBasolateral nuclear group (BLN) - lateral nucleus - La\t\t3653\t43170604.0\t17556\t43343.31426365281\t0.9556543498602063\t0.8580799006688026\t0.8653460664237342\n",
@@ -366,14 +355,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "f80d9e7e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:08:16.526721Z",
-     "iopub.status.busy": "2025-11-21T06:08:16.526532Z",
-     "iopub.status.idle": "2025-11-21T06:10:07.417420Z",
-     "shell.execute_reply": "2025-11-21T06:10:07.416475Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -381,258 +363,249 @@
      "text": [
       "decima - INFO - Using device: 0\n",
       "decima - INFO - Loading model v1_rep0 and metadata to compute attributions...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:00.8 (873.8MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.6 (1915.4MB/s)\n",
-      "/home/celikm5/Projects/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
-      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 334.69it/s]\n",
-      "Computing attributions...:   8%|█▎               | 4/50 [00:04<00:44,  1.04it/s]decima - WARNING - Gene XRCC5 has low correlation with the model. Pearson: 0.4621304058925145. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  10%|█▋               | 5/50 [00:05<00:41,  1.09it/s]decima - WARNING - Gene FNIP1 has low correlation with the model. Pearson: 0.044592478397006044. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  40%|██████▍         | 20/50 [00:17<00:25,  1.20it/s]decima - WARNING - Gene LRRFIP1 has low correlation with the model. Pearson: 0.4621130932978845. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  70%|███████████▏    | 35/50 [00:30<00:12,  1.20it/s]decima - WARNING - Gene CHD4 has low correlation with the model. Pearson: 0.27165706153002717. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...: 100%|████████████████| 50/50 [00:42<00:00,  1.17it/s]\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "/home/lala8/code/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 196.70it/s]\n",
+      "Computing attributions...:   8%|█▎               | 4/50 [00:05<00:53,  1.16s/it]decima - WARNING - Gene XRCC5 has low correlation with the model. Pearson: 0.4621304058925145. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  10%|█▋               | 5/50 [00:06<00:48,  1.08s/it]decima - WARNING - Gene FNIP1 has low correlation with the model. Pearson: 0.044592478397006044. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  40%|██████▍         | 20/50 [00:20<00:28,  1.04it/s]decima - WARNING - Gene LRRFIP1 has low correlation with the model. Pearson: 0.4621130932978845. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  70%|███████████▏    | 35/50 [00:34<00:14,  1.04it/s]decima - WARNING - Gene CHD4 has low correlation with the model. Pearson: 0.27165706153002717. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...: 100%|████████████████| 50/50 [00:49<00:00,  1.00it/s]\n",
       "decima - INFO - Loading metadata for model v1_rep0...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.6 (1927.1MB/s)\n",
-      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 317.61it/s]\n",
-      "Loading attributions and sequences...: 100%|████| 50/50 [00:08<00:00,  5.57it/s]\n",
-      "2025-11-25 12:07:48,118 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
-      "2025-11-25 12:07:48,122 - modisco-lite - INFO - Extracting seqlets for 50 tasks:\n",
-      "2025-11-25 12:07:48,123 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
-      "2025-11-25 12:07:48,133 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
-      "2025-11-25 12:07:48,190 - modisco-lite - INFO - - Computing isotonic thresholds\n",
-      "2025-11-25 12:07:48,220 - modisco-lite - INFO - - Refining thresholds\n",
-      "2025-11-25 12:07:48,516 - modisco-lite - INFO - - Extracting seqlets\n",
-      "2025-11-25 12:07:48,562 - modisco-lite - INFO - - Extracting 1299 positive seqlets\n",
-      "2025-11-25 12:07:48,569 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 1299 seqlets\n",
-      "100%|██████████████████████████████████████| 1299/1299 [00:05<00:00, 228.60it/s]\n",
-      "2025-11-25 12:08:01,695 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 1299 seqlets and 1299 neighbors\n",
-      "100%|██████████████████████████████████████| 1299/1299 [00:01<00:00, 983.48it/s]\n",
-      "100%|██████████████████████████████████████| 1299/1299 [00:01<00:00, 990.86it/s]\n",
-      "2025-11-25 12:08:04,426 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-25 12:08:05,151 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-25 12:08:07,633 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
+      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 202.12it/s]\n",
+      "Loading attributions and sequences...: 100%|████| 50/50 [00:09<00:00,  5.47it/s]\n",
+      "2026-05-24 22:09:11,395 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
+      "2026-05-24 22:09:11,400 - modisco-lite - INFO - Extracting seqlets for 50 tasks:\n",
+      "2026-05-24 22:09:11,400 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
+      "2026-05-24 22:09:11,416 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
+      "2026-05-24 22:09:11,493 - modisco-lite - INFO - - Computing isotonic thresholds\n",
+      "2026-05-24 22:09:11,533 - modisco-lite - INFO - - Refining thresholds\n",
+      "2026-05-24 22:09:11,915 - modisco-lite - INFO - - Extracting seqlets\n",
+      "2026-05-24 22:09:11,977 - modisco-lite - INFO - - Extracting 1296 positive seqlets\n",
+      "2026-05-24 22:09:11,985 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 1296 seqlets\n",
+      "100%|██████████████████████████████████████| 1296/1296 [00:05<00:00, 221.16it/s]\n",
+      "2026-05-24 22:09:26,432 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 1296 seqlets and 1296 neighbors\n",
+      "100%|██████████████████████████████████████| 1296/1296 [00:01<00:00, 812.49it/s]\n",
+      "100%|██████████████████████████████████████| 1296/1296 [00:01<00:00, 813.56it/s]\n",
+      "2026-05-24 22:09:29,765 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:09:30,596 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:09:33,963 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
       "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    7.7s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    7.9s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    7.9s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    9.2s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    9.8s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   10.7s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   10.8s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   11.5s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   11.7s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   12.8s remaining:    7.7s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   13.4s remaining:    6.1s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   13.8s remaining:    4.6s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   14.3s remaining:    3.3s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   15.1s remaining:    2.2s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   18.5s finished\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.1655701147360399\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.16556949666119894\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.16557106004365446\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.16557106004365446\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.16556096968779205\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.16556949666119894\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.16556880667793733\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.16557106004365446\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.16556880667793733\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.16556880667793733\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.16557106004365446\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.16556096968779205\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.16556880667793733\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.16556949666119894\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.1655701147360399\n",
-      "2025-11-25 12:08:26,241 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.16557106004365446\n",
-      "2025-11-25 12:08:26,244 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.86it/s]\n",
-      "2025-11-25 12:08:27,293 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1183 seqlets\n",
-      "100%|██████████████████████████████████████| 1183/1183 [00:01<00:00, 763.92it/s]\n",
-      "2025-11-25 12:08:29,362 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1183 seqlets and 1183 neighbors\n",
-      "100%|██████████████████████████████████████| 1183/1183 [00:03<00:00, 371.68it/s]\n",
-      "100%|██████████████████████████████████████| 1183/1183 [00:03<00:00, 371.15it/s]\n",
-      "2025-11-25 12:08:35,832 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-25 12:08:38,217 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    8.1s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    8.2s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    8.5s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    8.9s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   11.2s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   11.7s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   11.9s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   11.9s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   14.1s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   14.5s remaining:    8.7s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   14.6s remaining:    6.7s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   15.7s remaining:    5.2s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   17.2s remaining:    4.0s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   18.0s remaining:    2.6s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   18.8s finished\n",
+      "2026-05-24 22:09:52,817 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.16497841699037005\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.16497841699037005\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.16497841699037005\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.16497841699037005\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.16497841699037005\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.16497996248510152\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.16497841699037005\n",
+      "2026-05-24 22:09:52,818 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.16497841699037005\n",
+      "2026-05-24 22:09:52,822 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.50it/s]\n",
+      "2026-05-24 22:09:54,023 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1177 seqlets\n",
+      "100%|██████████████████████████████████████| 1177/1177 [00:01<00:00, 691.59it/s]\n",
+      "2026-05-24 22:09:56,334 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1177 seqlets and 1177 neighbors\n",
+      "100%|██████████████████████████████████████| 1177/1177 [00:03<00:00, 301.99it/s]\n",
+      "100%|██████████████████████████████████████| 1177/1177 [00:03<00:00, 302.12it/s]\n",
+      "2026-05-24 22:10:04,268 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:10:07,550 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    1.9s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    2.2s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    2.3s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    2.6s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    4.0s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    4.8s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    4.9s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    6.9s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    7.0s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    7.2s remaining:    4.3s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    9.4s remaining:    4.3s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:    9.8s remaining:    3.3s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   10.8s remaining:    2.5s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   11.6s remaining:    1.7s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   12.4s finished\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.14562863593931122\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.145634481216889\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.14562863593931122\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.14562863593931122\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.14564200148462944\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.14562640344312008\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.14564200148462944\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.1456238185758978\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.14564200148462944\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.14564200148462944\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.1456265966623099\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.145634481216889\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.14564200148462944\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.14564200148462944\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.145634481216889\n",
-      "2025-11-25 12:08:50,674 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.14564200148462944\n",
-      "2025-11-25 12:08:50,676 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.71it/s]\n",
-      "2025-11-25 12:08:51,784 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|████| 3/3 [00:15<00:00,  5.21s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
-      "2025-11-25 12:09:13,726 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|███████████████████| 24/24 [00:00<00:00, 22305.18it/s]\n",
-      "Computing subpatterns:: 100%|███████████████████| 16/16 [00:01<00:00, 11.34it/s]\n",
-      "2025-11-25 12:09:15,139 - modisco-lite - INFO - - Extracting 2162 negative seqlets\n",
-      "2025-11-25 12:09:15,150 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 2162 seqlets\n",
-      "100%|██████████████████████████████████████| 2162/2162 [00:05<00:00, 405.34it/s]\n",
-      "2025-11-25 12:09:22,736 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 2162 seqlets and 2162 neighbors\n",
-      "100%|██████████████████████████████████████| 2162/2162 [00:02<00:00, 988.09it/s]\n",
-      "100%|██████████████████████████████████████| 2162/2162 [00:02<00:00, 986.75it/s]\n",
-      "2025-11-25 12:09:27,274 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-25 12:09:28,462 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-25 12:09:32,639 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
-      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    8.4s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    8.6s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    9.0s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:   10.0s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   13.6s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   13.9s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   14.2s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   14.4s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   18.4s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   18.5s remaining:   11.1s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   18.7s remaining:    8.5s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   21.3s remaining:    7.1s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   22.9s remaining:    5.3s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   23.4s remaining:    3.3s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   25.7s finished\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.23030277739200716\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.2303035540754845\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.2314479078599401\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,459 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.2315149027759094\n",
-      "2025-11-25 12:09:58,463 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:01<00:00,  2.34it/s]\n",
-      "2025-11-25 12:10:00,172 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1847 seqlets\n",
-      "100%|██████████████████████████████████████| 1847/1847 [00:03<00:00, 510.45it/s]\n",
-      "2025-11-25 12:10:04,605 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1847 seqlets and 1847 neighbors\n",
-      "100%|██████████████████████████████████████| 1847/1847 [00:05<00:00, 367.14it/s]\n",
-      "100%|██████████████████████████████████████| 1847/1847 [00:05<00:00, 363.20it/s]\n",
-      "2025-11-25 12:10:14,881 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-25 12:10:18,848 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    2.6s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    2.7s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    2.8s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    3.3s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    5.4s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    5.6s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    6.3s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    6.6s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    8.2s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    8.4s remaining:    5.0s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    9.0s remaining:    4.1s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   10.0s remaining:    3.3s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   11.9s remaining:    2.7s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   12.5s remaining:    1.8s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   13.3s finished\n",
+      "2026-05-24 22:10:20,953 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,953 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,953 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.14574549731222797\n",
+      "2026-05-24 22:10:20,953 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.14575555412183777\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.1457554628491135\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.1457554628491135\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.14575386301623852\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.14574549731222797\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.14579461848630218\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.1457554628491135\n",
+      "2026-05-24 22:10:20,954 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.14575555412183777\n",
+      "2026-05-24 22:10:20,958 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.39it/s]\n",
+      "2026-05-24 22:10:22,215 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|████| 3/3 [00:20<00:00,  6.67s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
+      "2026-05-24 22:10:48,094 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|███████████████████| 25/25 [00:00<00:00, 17184.14it/s]\n",
+      "Computing subpatterns:: 100%|███████████████████| 20/20 [00:56<00:00,  2.80s/it]\n",
+      "2026-05-24 22:11:44,169 - modisco-lite - INFO - - Extracting 2165 negative seqlets\n",
+      "2026-05-24 22:11:44,184 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 2165 seqlets\n",
+      "100%|██████████████████████████████████████| 2165/2165 [00:06<00:00, 350.55it/s]\n",
+      "2026-05-24 22:11:52,971 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 2165 seqlets and 2165 neighbors\n",
+      "100%|██████████████████████████████████████| 2165/2165 [00:02<00:00, 812.62it/s]\n",
+      "100%|██████████████████████████████████████| 2165/2165 [00:02<00:00, 809.07it/s]\n",
+      "2026-05-24 22:11:58,536 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:11:59,919 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:12:05,619 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
+      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   16.0s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:   16.9s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:   17.8s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:   17.8s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   23.3s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   24.1s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   24.1s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   25.5s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   30.0s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   31.0s remaining:   18.6s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   32.0s remaining:   14.5s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   33.8s remaining:   11.3s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   36.0s remaining:    8.3s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   37.7s remaining:    5.4s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   41.1s finished\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.23195018462869912\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.23199174090026503\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.23199393024163317\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.23199483991520736\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.23199174090026503\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.2319922332251735\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.23199458884947163\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.23058865479166565\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.23199458884947163\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.23193260828361367\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.23195018462869912\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.23195018462869912\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.23195018462869912\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.23199458884947163\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.23199130761510758\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.2319908975207633\n",
+      "2026-05-24 22:12:46,786 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:01<00:00,  2.18it/s]\n",
+      "2026-05-24 22:12:48,622 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1855 seqlets\n",
+      "100%|██████████████████████████████████████| 1855/1855 [00:04<00:00, 440.76it/s]\n",
+      "2026-05-24 22:12:53,789 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1855 seqlets and 1855 neighbors\n",
+      "100%|██████████████████████████████████████| 1855/1855 [00:06<00:00, 303.87it/s]\n",
+      "100%|██████████████████████████████████████| 1855/1855 [00:06<00:00, 303.80it/s]\n",
+      "2026-05-24 22:13:06,314 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:13:11,804 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    3.8s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    4.2s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    5.4s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    5.5s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    8.5s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    8.9s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   10.0s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   11.2s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   12.0s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   13.8s remaining:    8.3s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   14.6s remaining:    6.6s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   15.9s remaining:    5.3s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   16.8s remaining:    3.9s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   17.8s remaining:    2.5s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   20.9s finished\n",
-      "2025-11-25 12:10:39,872 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.19489312964734903\n",
-      "2025-11-25 12:10:39,872 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.19487036751957057\n",
-      "2025-11-25 12:10:39,872 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.19489312964734903\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.19489529999573643\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.19484005269342827\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.19489312964734903\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.19483674741628879\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.19489312964734903\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.19487869771192487\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.19487609321608304\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.19483674741628879\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.19483674741628879\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.19487609321608304\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.1948266833439534\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.19393479046208167\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.19489529999573643\n",
-      "2025-11-25 12:10:39,873 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:01<00:00,  2.06it/s]\n",
-      "2025-11-25 12:10:41,817 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|████| 4/4 [00:18<00:00,  4.64s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
-      "2025-11-25 12:11:10,053 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|███████████████████| 24/24 [00:00<00:00, 17985.22it/s]\n",
-      "Computing subpatterns:: 100%|███████████████████| 21/21 [00:04<00:00,  4.95it/s]\n",
-      "Creating modisco logos for pos_patterns: 100%|██| 16/16 [00:22<00:00,  1.38s/it]\n",
-      "Creating modisco logos for neg_patterns: 100%|██| 21/21 [00:43<00:00,  2.05s/it]\n",
-      "Generating patterns dataframe: 100%|█████████████| 2/2 [00:00<00:00, 225.69it/s]\n",
-      "Reading patterns for pos_patterns: 100%|██████| 16/16 [00:00<00:00, 3155.98it/s]\n",
-      "Reading patterns for neg_patterns: 100%|██████| 21/21 [00:00<00:00, 3204.44it/s]\n",
-      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    5.2s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    5.9s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    6.7s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    7.1s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   12.4s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   13.4s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   14.2s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   14.2s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   24.6s\n",
-      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:   26.3s\n",
-      "[Parallel(n_jobs=4)]: Done  11 tasks      | elapsed:   51.2s\n",
-      "[Parallel(n_jobs=4)]: Done  12 tasks      | elapsed:   53.7s\n",
-      "[Parallel(n_jobs=4)]: Done  13 tasks      | elapsed:  1.3min\n",
-      "[Parallel(n_jobs=4)]: Done  14 tasks      | elapsed:  1.3min\n",
-      "[Parallel(n_jobs=4)]: Done  15 tasks      | elapsed:  1.4min\n",
-      "[Parallel(n_jobs=4)]: Done  16 tasks      | elapsed:  1.4min\n",
-      "[Parallel(n_jobs=4)]: Done  17 tasks      | elapsed:  1.8min\n",
-      "[Parallel(n_jobs=4)]: Done  18 tasks      | elapsed:  2.0min\n",
-      "[Parallel(n_jobs=4)]: Done  19 tasks      | elapsed:  2.0min\n",
-      "[Parallel(n_jobs=4)]: Done  20 tasks      | elapsed:  2.0min\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    4.8s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    5.6s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    6.2s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    7.3s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    9.6s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   10.9s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   12.4s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   14.0s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   14.6s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   15.7s remaining:    9.4s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   18.2s remaining:    8.3s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   20.4s remaining:    6.8s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   20.5s remaining:    4.7s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   21.4s remaining:    3.1s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   26.5s finished\n",
+      "2026-05-24 22:13:38,459 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.19503089441890722\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.1951388122194609\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.19512814860921504\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.19512750182351285\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.19512814860921504\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.19503108318529697\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.19503108318529697\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.1950650084649078\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.19513013154528258\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.19503108318529697\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.19503108318529697\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.19513788476833965\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.19503022821790758\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.19443545749556165\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.19512814860921504\n",
+      "2026-05-24 22:13:38,460 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.19513423092930496\n",
+      "2026-05-24 22:13:38,464 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:01<00:00,  2.14it/s]\n",
+      "2026-05-24 22:13:40,337 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|████| 4/4 [00:25<00:00,  6.37s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
+      "2026-05-24 22:14:15,178 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|███████████████████| 22/22 [00:00<00:00, 14616.61it/s]\n",
+      "Computing subpatterns:: 100%|███████████████████| 20/20 [00:53<00:00,  2.70s/it]\n",
+      "Creating modisco logos for pos_patterns: 100%|██| 20/20 [00:29<00:00,  1.46s/it]\n",
+      "Creating modisco logos for neg_patterns: 100%|██| 20/20 [00:40<00:00,  2.01s/it]\n",
+      "Generating patterns dataframe: 100%|█████████████| 2/2 [00:00<00:00, 238.58it/s]\n",
+      "Reading patterns for pos_patterns: 100%|██████| 20/20 [00:00<00:00, 3380.73it/s]\n",
+      "Reading patterns for neg_patterns: 100%|██████| 20/20 [00:00<00:00, 3436.26it/s]\n",
+      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   10.0s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:   10.2s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:   10.4s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:   11.0s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   11.2s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   11.5s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   14.3s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   21.7s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   23.4s\n",
+      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:   25.8s\n",
+      "[Parallel(n_jobs=4)]: Done  11 tasks      | elapsed:   39.4s\n",
+      "[Parallel(n_jobs=4)]: Done  12 tasks      | elapsed:   53.3s\n",
+      "[Parallel(n_jobs=4)]: Done  13 tasks      | elapsed:  1.5min\n",
+      "[Parallel(n_jobs=4)]: Done  14 tasks      | elapsed:  1.6min\n",
+      "[Parallel(n_jobs=4)]: Done  15 tasks      | elapsed:  1.6min\n",
+      "[Parallel(n_jobs=4)]: Done  16 tasks      | elapsed:  1.7min\n",
+      "[Parallel(n_jobs=4)]: Done  17 tasks      | elapsed:  2.0min\n",
+      "[Parallel(n_jobs=4)]: Done  18 tasks      | elapsed:  2.1min\n",
+      "[Parallel(n_jobs=4)]: Done  19 tasks      | elapsed:  2.1min\n",
+      "[Parallel(n_jobs=4)]: Done  20 tasks      | elapsed:  2.1min\n",
       "[Parallel(n_jobs=4)]: Done  21 tasks      | elapsed:  2.1min\n",
-      "[Parallel(n_jobs=4)]: Done  22 tasks      | elapsed:  2.2min\n",
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-      "[Parallel(n_jobs=4)]: Done  28 tasks      | elapsed:  3.2min\n",
-      "[Parallel(n_jobs=4)]: Done  29 tasks      | elapsed:  3.2min\n",
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-      "[Parallel(n_jobs=4)]: Done  37 out of  37 | elapsed:  4.5min finished\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.7 (1878.0MB/s)\n",
-      "Processing pos_patterns patterns...: 100%|█████| 16/16 [00:00<00:00, 172.33it/s]\n",
-      "Processing neg_patterns patterns...: 100%|█████| 21/21 [00:00<00:00, 575.43it/s]\n",
+      "[Parallel(n_jobs=4)]: Done  22 tasks      | elapsed:  2.1min\n",
+      "[Parallel(n_jobs=4)]: Done  23 tasks      | elapsed:  2.7min\n",
+      "[Parallel(n_jobs=4)]: Done  24 tasks      | elapsed:  2.7min\n",
+      "[Parallel(n_jobs=4)]: Done  25 tasks      | elapsed:  2.8min\n",
+      "[Parallel(n_jobs=4)]: Done  26 tasks      | elapsed:  2.9min\n",
+      "[Parallel(n_jobs=4)]: Done  27 tasks      | elapsed:  2.9min\n",
+      "[Parallel(n_jobs=4)]: Done  28 tasks      | elapsed:  2.9min\n",
+      "[Parallel(n_jobs=4)]: Done  29 tasks      | elapsed:  2.9min\n",
+      "[Parallel(n_jobs=4)]: Done  30 tasks      | elapsed:  3.0min\n",
+      "[Parallel(n_jobs=4)]: Done  31 tasks      | elapsed:  3.6min\n",
+      "[Parallel(n_jobs=4)]: Done  32 tasks      | elapsed:  3.6min\n",
+      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:  3.6min\n",
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+      "[Parallel(n_jobs=4)]: Done  38 out of  40 | elapsed:  5.1min remaining:   16.0s\n",
+      "[Parallel(n_jobs=4)]: Done  40 out of  40 | elapsed:  5.5min finished\n",
+      "Processing pos_patterns patterns...: 100%|█████| 20/20 [00:00<00:00, 611.27it/s]\n",
+      "Processing neg_patterns patterns...: 100%|█████| 20/20 [00:00<00:00, 597.09it/s]\n",
       "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
      ]
     }
@@ -667,14 +640,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "736a6be2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:10:07.419004Z",
-     "iopub.status.busy": "2025-11-21T06:10:07.418854Z",
-     "iopub.status.idle": "2025-11-21T06:10:07.561463Z",
-     "shell.execute_reply": "2025-11-21T06:10:07.560470Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -687,44 +653,51 @@
       "example/modisco_neurons.warnings.qc.log\n",
       "\n",
       "example/modisco_neurons_report:\n",
-      "ARNT2.H13CORE.0.P.B.png       PAX4.H13CORE.1.S.C.png\n",
-      "ARNT.H13CORE.1.P.B.png\t      PO3F2.H13CORE.1.S.B.png\n",
-      "ATF2.H13CORE.1.P.B.png\t      PO4F2.H13CORE.0.S.B.png\n",
-      "ATF3.H13CORE.2.P.B.png\t      PO6F1.H13CORE.0.SM.B.png\n",
-      "ATF4.H13CORE.0.P.B.png\t      RORB.H13CORE.0.SM.B.png\n",
-      "ATF4.H13CORE.1.S.B.png\t      SATB1.H13CORE.0.P.B.png\n",
-      "BACH2.H13CORE.1.SM.B.png      SNAI2.H13CORE.0.PSM.A.png\n",
-      "BATF2.H13CORE.0.PSG.A.png     SOX11.H13CORE.0.P.B.png\n",
-      "CEBPG.H13CORE.2.P.B.png       SP140L.H13CORE.0.PSGIB.A.png\n",
-      "CLOCK.H13CORE.1.PS.A.png      SP2.H13CORE.1.P.B.png\n",
-      "CREB1.H13CORE.0.PSM.A.png     SRBP1.H13CORE.0.P.B.png\n",
-      "DDIT3.H13CORE.0.P.B.png       STAT1.H13CORE.1.P.B.png\n",
-      "ELK4.H13CORE.0.PSM.A.png      STAT2.H13CORE.0.P.B.png\n",
-      "ERR3.H13CORE.0.PSM.A.png      STF1.H13CORE.0.PSM.A.png\n",
-      "FEV.H13CORE.0.S.B.png\t      TAF1.H13CORE.0.P.B.png\n",
-      "FLI1.H13CORE.0.PSM.A.png      TEAD1.H13CORE.1.P.C.png\n",
-      "FOXJ2.H13CORE.1.SM.B.png      trimmed_logos\n",
-      "IRF4.H13CORE.0.P.B.png\t      TYY2.H13CORE.0.PS.A.png\n",
-      "IRF4.H13CORE.1.S.B.png\t      YBOX1.H13CORE.0.SM.B.png\n",
-      "IRF7.H13CORE.1.SM.B.png       Z354A.H13CORE.0.P.B.png\n",
-      "IRF8.H13CORE.0.P.B.png\t      ZBT44.H13CORE.0.P.C.png\n",
-      "ITF2.H13CORE.1.PSM.A.png      ZBTB40.H13CORE.0.PSG.A.png\n",
-      "KLF16.H13CORE.1.P.B.png       ZEB1.H13CORE.0.P.B.png\n",
-      "KLF9.H13CORE.1.P.B.png\t      ZEB2.H13CORE.0.P.B.png\n",
-      "KMT2A.H13CORE.0.P.B.png       ZN266.H13CORE.0.P.B.png\n",
-      "KMT2B.H13CORE.0.P.B.png       ZN281.H13CORE.1.SM.B.png\n",
-      "LMX1B.H13CORE.0.P.C.png       ZN483.H13CORE.0.P.C.png\n",
-      "LMX1B.H13CORE.2.S.B.png       ZN519.H13CORE.0.P.C.png\n",
-      "motifs.html\t\t      ZN529.H13CORE.0.P.B.png\n",
-      "MSANTD4.H13CORE.0.SGIB.A.png  ZN616.H13CORE.0.P.C.png\n",
-      "MYSM1.H13CORE.0.P.B.png       ZN740.H13CORE.0.P.C.png\n",
-      "MYT1L.H13CORE.0.P.C.png       ZN740.H13CORE.1.S.C.png\n",
-      "NFAC2.H13CORE.0.P.B.png       ZN776.H13CORE.0.P.C.png\n",
-      "NFE2.H13CORE.1.SM.B.png       ZN778.H13CORE.1.P.B.png\n",
-      "NR1I2.H13CORE.1.S.C.png       ZN784.H13CORE.0.SM.B.png\n",
-      "NR1I3.H13CORE.1.PSM.A.png     ZNF292.H13CORE.0.PSG.A.png\n",
-      "NR5A2.H13CORE.0.PSM.A.png     ZNF618.H13CORE.0.G.B.png\n",
-      "PATZ1.H13CORE.0.P.B.png\n"
+      "ALX3.H13CORE.0.SM.B.png       SIX2.H13CORE.0.P.B.png\n",
+      "ATF1.H13CORE.0.P.B.png\t      SNAI2.H13CORE.0.PSM.A.png\n",
+      "ATF1.H13CORE.1.P.B.png\t      SOX2.H13CORE.3.S.B.png\n",
+      "ATF4.H13CORE.1.S.B.png\t      SP2.H13CORE.1.P.B.png\n",
+      "BACH2.H13CORE.1.SM.B.png      SP3.H13CORE.0.P.B.png\n",
+      "CEBPG.H13CORE.0.P.B.png       SPIB.H13CORE.0.P.B.png\n",
+      "CEBPG.H13CORE.2.P.B.png       STAT1.H13CORE.1.P.B.png\n",
+      "CPEB1.H13CORE.1.S.B.png       STAT2.H13CORE.0.P.B.png\n",
+      "CREB1.H13CORE.0.PSM.A.png     STF1.H13CORE.0.PSM.A.png\n",
+      "CUX1.H13CORE.0.PM.A.png       TAF1.H13CORE.0.P.B.png\n",
+      "ERG.H13CORE.0.P.B.png\t      TBX5.H13CORE.2.S.B.png\n",
+      "ERR3.H13CORE.0.PSM.A.png      TET3.H13CORE.0.PSGIB.A.png\n",
+      "ESX1.H13CORE.1.S.B.png\t      TFEC.H13CORE.0.M.B.png\n",
+      "ETS2.H13CORE.1.P.B.png\t      trimmed_logos\n",
+      "ETV6.H13CORE.0.PS.A.png       TYY1.H13CORE.0.PSM.A.png\n",
+      "GLIS3.H13CORE.1.S.C.png       TYY1.H13CORE.1.S.B.png\n",
+      "GMEB2.H13CORE.0.P.C.png       TYY2.H13CORE.0.PS.A.png\n",
+      "HSF1.H13CORE.1.P.B.png\t      TYY2.H13CORE.1.SM.B.png\n",
+      "IRF1.H13CORE.0.P.B.png\t      ZBT14.H13CORE.0.P.C.png\n",
+      "IRF4.H13CORE.1.S.B.png\t      ZBT40.H13CORE.0.P.B.png\n",
+      "IRF8.H13CORE.0.P.B.png\t      ZEB1.H13CORE.0.P.B.png\n",
+      "ITF2.H13CORE.1.PSM.A.png      ZEB2.H13CORE.0.P.B.png\n",
+      "KLF16.H13CORE.1.P.B.png       ZFP37.H13CORE.0.P.C.png\n",
+      "KLF9.H13CORE.1.P.B.png\t      ZKSC5.H13CORE.0.P.C.png\n",
+      "KMT2A.H13CORE.0.P.B.png       ZN100.H13CORE.1.P.B.png\n",
+      "MAZ.H13CORE.1.P.B.png\t      ZN254.H13CORE.0.P.C.png\n",
+      "motifs.html\t\t      ZN266.H13CORE.0.P.B.png\n",
+      "MSANTD4.H13CORE.0.SGIB.A.png  ZN362.H13CORE.0.P.C.png\n",
+      "MYT1.H13CORE.0.SG.A.png       ZN432.H13CORE.0.P.C.png\n",
+      "MYT1L.H13CORE.0.P.C.png       ZN449.H13CORE.1.S.C.png\n",
+      "NANOG.H13CORE.0.P.B.png       ZN454.H13CORE.1.SM.B.png\n",
+      "NFAC1.H13CORE.0.P.B.png       ZN483.H13CORE.0.P.C.png\n",
+      "NFAC3.H13CORE.1.P.C.png       ZN519.H13CORE.0.P.C.png\n",
+      "NFE2.H13CORE.1.SM.B.png       ZN613.H13CORE.0.P.C.png\n",
+      "NFIL3.H13CORE.0.P.B.png       ZN616.H13CORE.0.P.C.png\n",
+      "NR1D1.H13CORE.0.P.B.png       ZN664.H13CORE.1.P.C.png\n",
+      "NR1I2.H13CORE.0.M.C.png       ZN784.H13CORE.0.SM.B.png\n",
+      "NR1I2.H13CORE.1.S.C.png       ZN805.H13CORE.0.P.C.png\n",
+      "NR5A2.H13CORE.0.PSM.A.png     ZN880.H13CORE.0.P.C.png\n",
+      "P5F1B.H13CORE.0.S.B.png       ZNF292.H13CORE.0.PSG.A.png\n",
+      "PIT1.H13CORE.0.SM.B.png       ZNF575.H13CORE.0.PSG.A.png\n",
+      "RUNX3.H13CORE.1.S.B.png       ZNF66.H13CORE.0.PG.A.png\n",
+      "SALL3.H13CORE.0.PSG.A.png     ZNF70.H13CORE.0.PSG.A.png\n",
+      "SATB1.H13CORE.0.P.B.png       ZSCAN25.H13CORE.0.PSG.A.png\n",
+      "SIX1.H13CORE.0.P.B.png\n"
      ]
     }
    ],
@@ -744,14 +717,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "caf89100",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:10:07.563276Z",
-     "iopub.status.busy": "2025-11-21T06:10:07.563099Z",
-     "iopub.status.idle": "2025-11-21T06:10:07.803904Z",
-     "shell.execute_reply": "2025-11-21T06:10:07.803087Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -777,558 +743,603 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_0</td>\n",
-       "      <td>92</td>\n",
+       "      <td>91</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_0.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_0.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZN529.H13CORE.0.P.B</td>\n",
-       "      <td>0.086240</td>\n",
-       "      <td><img src=\"ZN529.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>PO6F1.H13CORE.0.SM.B</td>\n",
-       "      <td>0.336992</td>\n",
-       "      <td><img src=\"PO6F1.H13CORE.0.SM.B.png\" width=\"240\" ></td>\n",
-       "      <td>SOX11.H13CORE.0.P.B</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"SOX11.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>PIT1.H13CORE.0.SM.B</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"PIT1.H13CORE.0.SM.B.png\" width=\"240\" ></td>\n",
+       "      <td>CEBPG.H13CORE.2.P.B</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"CEBPG.H13CORE.2.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN805.H13CORE.0.P.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZN805.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_1</td>\n",
-       "      <td>76</td>\n",
+       "      <td>83</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_1.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_1.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZN776.H13CORE.0.P.C</td>\n",
-       "      <td>0.517371</td>\n",
-       "      <td><img src=\"ZN776.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "      <td>ATF4.H13CORE.1.S.B</td>\n",
-       "      <td>0.517371</td>\n",
+       "      <td>3.708310e-04</td>\n",
        "      <td><img src=\"ATF4.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
-       "      <td>DDIT3.H13CORE.0.P.B</td>\n",
-       "      <td>0.517371</td>\n",
-       "      <td><img src=\"DDIT3.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>CEBPG.H13CORE.0.P.B</td>\n",
+       "      <td>1.399430e-03</td>\n",
+       "      <td><img src=\"CEBPG.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>CEBPG.H13CORE.2.P.B</td>\n",
+       "      <td>1.399430e-03</td>\n",
+       "      <td><img src=\"CEBPG.H13CORE.2.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_2</td>\n",
-       "      <td>74</td>\n",
+       "      <td>72</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_2.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_2.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ZN519.H13CORE.0.P.C</td>\n",
-       "      <td>0.001973</td>\n",
-       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2B.H13CORE.0.P.B</td>\n",
-       "      <td>0.010821</td>\n",
-       "      <td><img src=\"KMT2B.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>CUX1.H13CORE.0.PM.A</td>\n",
+       "      <td>6.300970e-01</td>\n",
+       "      <td><img src=\"CUX1.H13CORE.0.PM.A.png\" width=\"240\" ></td>\n",
+       "      <td>TBX5.H13CORE.2.S.B</td>\n",
+       "      <td>7.081360e-01</td>\n",
+       "      <td><img src=\"TBX5.H13CORE.2.S.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN664.H13CORE.1.P.C</td>\n",
+       "      <td>7.081360e-01</td>\n",
+       "      <td><img src=\"ZN664.H13CORE.1.P.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_3</td>\n",
-       "      <td>73</td>\n",
+       "      <td>67</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_3.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_3.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ATF4.H13CORE.0.P.B</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"ATF4.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>BATF2.H13CORE.0.PSG.A</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"BATF2.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
-       "      <td>YBOX1.H13CORE.0.SM.B</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"YBOX1.H13CORE.0.SM.B.png\" width=\"240\" ></td>\n",
+       "      <td>ITF2.H13CORE.1.PSM.A</td>\n",
+       "      <td>3.958280e-02</td>\n",
+       "      <td><img src=\"ITF2.H13CORE.1.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>ZEB2.H13CORE.0.P.B</td>\n",
+       "      <td>3.958280e-02</td>\n",
+       "      <td><img src=\"ZEB2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>SNAI2.H13CORE.0.PSM.A</td>\n",
+       "      <td>3.958280e-02</td>\n",
+       "      <td><img src=\"SNAI2.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_4</td>\n",
-       "      <td>71</td>\n",
+       "      <td>66</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_4.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_4.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ATF4.H13CORE.1.S.B</td>\n",
-       "      <td>0.001862</td>\n",
-       "      <td><img src=\"ATF4.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
-       "      <td>ATF3.H13CORE.2.P.B</td>\n",
-       "      <td>0.002186</td>\n",
-       "      <td><img src=\"ATF3.H13CORE.2.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>CEBPG.H13CORE.2.P.B</td>\n",
-       "      <td>0.002186</td>\n",
-       "      <td><img src=\"CEBPG.H13CORE.2.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>SOX2.H13CORE.3.S.B</td>\n",
+       "      <td>7.978900e-01</td>\n",
+       "      <td><img src=\"SOX2.H13CORE.3.S.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZFP37.H13CORE.0.P.C</td>\n",
+       "      <td>7.978900e-01</td>\n",
+       "      <td><img src=\"ZFP37.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>SALL3.H13CORE.0.PSG.A</td>\n",
+       "      <td>7.978900e-01</td>\n",
+       "      <td><img src=\"SALL3.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_5</td>\n",
-       "      <td>68</td>\n",
+       "      <td>59</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_5.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_5.cwm.rev.png\" width=\"240\" ></td>\n",
        "      <td>ZEB2.H13CORE.0.P.B</td>\n",
-       "      <td>0.472675</td>\n",
+       "      <td>8.308970e-01</td>\n",
        "      <td><img src=\"ZEB2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "      <td>ZEB1.H13CORE.0.P.B</td>\n",
-       "      <td>0.472675</td>\n",
+       "      <td>8.308970e-01</td>\n",
        "      <td><img src=\"ZEB1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ITF2.H13CORE.1.PSM.A</td>\n",
-       "      <td>0.472675</td>\n",
-       "      <td><img src=\"ITF2.H13CORE.1.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>ZN784.H13CORE.0.SM.B</td>\n",
+       "      <td>8.308970e-01</td>\n",
+       "      <td><img src=\"ZN784.H13CORE.0.SM.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_6</td>\n",
-       "      <td>67</td>\n",
+       "      <td>57</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_6.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_6.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>FOXJ2.H13CORE.1.SM.B</td>\n",
-       "      <td>0.283216</td>\n",
-       "      <td><img src=\"FOXJ2.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
-       "      <td>ATF2.H13CORE.1.P.B</td>\n",
-       "      <td>0.283216</td>\n",
-       "      <td><img src=\"ATF2.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ZBTB40.H13CORE.0.PSG.A</td>\n",
-       "      <td>0.339175</td>\n",
-       "      <td><img src=\"ZBTB40.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
+       "      <td>ZN483.H13CORE.0.P.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZN483.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>ZN880.H13CORE.0.P.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZN880.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>ZNF292.H13CORE.0.PSG.A</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZNF292.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_7</td>\n",
-       "      <td>66</td>\n",
+       "      <td>51</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_7.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_7.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KLF16.H13CORE.1.P.B</td>\n",
-       "      <td>0.001949</td>\n",
-       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>SP2.H13CORE.1.P.B</td>\n",
-       "      <td>0.001949</td>\n",
-       "      <td><img src=\"SP2.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.001949</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>TET3.H13CORE.0.PSGIB.A</td>\n",
+       "      <td>5.120360e-04</td>\n",
+       "      <td><img src=\"TET3.H13CORE.0.PSGIB.A.png\" width=\"240\" ></td>\n",
+       "      <td>ZSCAN25.H13CORE.0.PSG.A</td>\n",
+       "      <td>3.452800e-03</td>\n",
+       "      <td><img src=\"ZSCAN25.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
+       "      <td>MAZ.H13CORE.1.P.B</td>\n",
+       "      <td>2.605450e-02</td>\n",
+       "      <td><img src=\"MAZ.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_8</td>\n",
-       "      <td>66</td>\n",
+       "      <td>51</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_8.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_8.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZN483.H13CORE.0.P.C</td>\n",
-       "      <td>0.175568</td>\n",
-       "      <td><img src=\"ZN483.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
-       "      <td>ZNF292.H13CORE.0.PSG.A</td>\n",
-       "      <td>0.336695</td>\n",
-       "      <td><img src=\"ZNF292.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
-       "      <td>ZBT44.H13CORE.0.P.C</td>\n",
-       "      <td>0.537918</td>\n",
-       "      <td><img src=\"ZBT44.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
+       "      <td>7.135100e-05</td>\n",
+       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN519.H13CORE.0.P.C</td>\n",
+       "      <td>2.300240e-04</td>\n",
+       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>KLF16.H13CORE.1.P.B</td>\n",
+       "      <td>1.671670e-03</td>\n",
+       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_9</td>\n",
-       "      <td>61</td>\n",
+       "      <td>48</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_9.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_9.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZEB2.H13CORE.0.P.B</td>\n",
-       "      <td>0.053112</td>\n",
-       "      <td><img src=\"ZEB2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ITF2.H13CORE.1.PSM.A</td>\n",
-       "      <td>0.053112</td>\n",
-       "      <td><img src=\"ITF2.H13CORE.1.PSM.A.png\" width=\"240\" ></td>\n",
-       "      <td>SNAI2.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.053112</td>\n",
-       "      <td><img src=\"SNAI2.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>NFIL3.H13CORE.0.P.B</td>\n",
+       "      <td>2.666570e-01</td>\n",
+       "      <td><img src=\"NFIL3.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>NANOG.H13CORE.0.P.B</td>\n",
+       "      <td>2.666570e-01</td>\n",
+       "      <td><img src=\"NANOG.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>P5F1B.H13CORE.0.S.B</td>\n",
+       "      <td>2.666570e-01</td>\n",
+       "      <td><img src=\"P5F1B.H13CORE.0.S.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_10</td>\n",
-       "      <td>59</td>\n",
+       "      <td>46</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_10.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_10.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ARNT2.H13CORE.0.P.B</td>\n",
-       "      <td>0.091374</td>\n",
-       "      <td><img src=\"ARNT2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>CLOCK.H13CORE.1.PS.A</td>\n",
-       "      <td>0.091374</td>\n",
-       "      <td><img src=\"CLOCK.H13CORE.1.PS.A.png\" width=\"240\" ></td>\n",
-       "      <td>TEAD1.H13CORE.1.P.C</td>\n",
-       "      <td>0.900591</td>\n",
-       "      <td><img src=\"TEAD1.H13CORE.1.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>TYY1.H13CORE.1.S.B</td>\n",
+       "      <td>4.215170e-02</td>\n",
+       "      <td><img src=\"TYY1.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
+       "      <td>TYY2.H13CORE.0.PS.A</td>\n",
+       "      <td>5.291280e-02</td>\n",
+       "      <td><img src=\"TYY2.H13CORE.0.PS.A.png\" width=\"240\" ></td>\n",
+       "      <td>TYY1.H13CORE.0.PSM.A</td>\n",
+       "      <td>5.692470e-02</td>\n",
+       "      <td><img src=\"TYY1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_11</td>\n",
-       "      <td>43</td>\n",
+       "      <td>45</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_11.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_11.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>KLF16.H13CORE.1.P.B</td>\n",
-       "      <td>0.000018</td>\n",
-       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>KLF9.H13CORE.1.P.B</td>\n",
-       "      <td>0.000018</td>\n",
-       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>NR5A2.H13CORE.0.PSM.A</td>\n",
+       "      <td>4.466050e-03</td>\n",
+       "      <td><img src=\"NR5A2.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>STF1.H13CORE.0.PSM.A</td>\n",
+       "      <td>4.466050e-03</td>\n",
+       "      <td><img src=\"STF1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>ERR3.H13CORE.0.PSM.A</td>\n",
+       "      <td>4.466050e-03</td>\n",
+       "      <td><img src=\"ERR3.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_12</td>\n",
-       "      <td>43</td>\n",
+       "      <td>44</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_12.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_12.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>NR5A2.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.011962</td>\n",
-       "      <td><img src=\"NR5A2.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
-       "      <td>ERR3.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.011962</td>\n",
-       "      <td><img src=\"ERR3.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
-       "      <td>STF1.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.011962</td>\n",
-       "      <td><img src=\"STF1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>ZN519.H13CORE.0.P.C</td>\n",
+       "      <td>1.183130e-03</td>\n",
+       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
+       "      <td>1.183130e-03</td>\n",
+       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZNF575.H13CORE.0.PSG.A</td>\n",
+       "      <td>1.357830e-01</td>\n",
+       "      <td><img src=\"ZNF575.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_13</td>\n",
-       "      <td>34</td>\n",
+       "      <td>40</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_13.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_13.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZN740.H13CORE.1.S.C</td>\n",
-       "      <td>0.031638</td>\n",
-       "      <td><img src=\"ZN740.H13CORE.1.S.C.png\" width=\"240\" ></td>\n",
-       "      <td>ZN281.H13CORE.1.SM.B</td>\n",
-       "      <td>0.031638</td>\n",
-       "      <td><img src=\"ZN281.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
-       "      <td>ZN740.H13CORE.0.P.C</td>\n",
-       "      <td>0.031638</td>\n",
-       "      <td><img src=\"ZN740.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
+       "      <td>2.149500e-05</td>\n",
+       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN519.H13CORE.0.P.C</td>\n",
+       "      <td>3.650270e-04</td>\n",
+       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>KLF9.H13CORE.1.P.B</td>\n",
+       "      <td>2.395360e-03</td>\n",
+       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_14</td>\n",
        "      <td>32</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_14.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_14.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>PATZ1.H13CORE.0.P.B</td>\n",
-       "      <td>0.028355</td>\n",
-       "      <td><img src=\"PATZ1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.039733</td>\n",
+       "      <td>7.891810e-03</td>\n",
        "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ARNT.H13CORE.1.P.B</td>\n",
-       "      <td>0.086674</td>\n",
-       "      <td><img src=\"ARNT.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>KLF9.H13CORE.1.P.B</td>\n",
+       "      <td>7.622300e-02</td>\n",
+       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN519.H13CORE.0.P.C</td>\n",
+       "      <td>1.089170e-01</td>\n",
+       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>pos_patterns.pattern_15</td>\n",
-       "      <td>27</td>\n",
+       "      <td>30</td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_15.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/pos_patterns.pattern_15.cwm.rev.png\" width=\"240\" ></td>\n",
        "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.000015</td>\n",
+       "      <td>1.508690e-03</td>\n",
        "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "      <td>ZN519.H13CORE.0.P.C</td>\n",
-       "      <td>0.000410</td>\n",
+       "      <td>3.196670e-03</td>\n",
        "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "      <td>KLF9.H13CORE.1.P.B</td>\n",
-       "      <td>0.009287</td>\n",
+       "      <td>2.249850e-02</td>\n",
        "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <td>pos_patterns.pattern_16</td>\n",
+       "      <td>29</td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_16.cwm.fwd.png\" width=\"240\" ></td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_16.cwm.rev.png\" width=\"240\" ></td>\n",
+       "      <td>ZN100.H13CORE.1.P.B</td>\n",
+       "      <td>9.710870e-01</td>\n",
+       "      <td><img src=\"ZN100.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>TYY1.H13CORE.0.PSM.A</td>\n",
+       "      <td>9.710870e-01</td>\n",
+       "      <td><img src=\"TYY1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>TYY2.H13CORE.0.PS.A</td>\n",
+       "      <td>9.710870e-01</td>\n",
+       "      <td><img src=\"TYY2.H13CORE.0.PS.A.png\" width=\"240\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>pos_patterns.pattern_17</td>\n",
+       "      <td>29</td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_17.cwm.fwd.png\" width=\"240\" ></td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_17.cwm.rev.png\" width=\"240\" ></td>\n",
+       "      <td>ZNF66.H13CORE.0.PG.A</td>\n",
+       "      <td>2.199250e-01</td>\n",
+       "      <td><img src=\"ZNF66.H13CORE.0.PG.A.png\" width=\"240\" ></td>\n",
+       "      <td>ZEB2.H13CORE.0.P.B</td>\n",
+       "      <td>2.199250e-01</td>\n",
+       "      <td><img src=\"ZEB2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>NR1D1.H13CORE.0.P.B</td>\n",
+       "      <td>2.199250e-01</td>\n",
+       "      <td><img src=\"NR1D1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>pos_patterns.pattern_18</td>\n",
+       "      <td>24</td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_18.cwm.fwd.png\" width=\"240\" ></td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_18.cwm.rev.png\" width=\"240\" ></td>\n",
+       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
+       "      <td>7.308550e-03</td>\n",
+       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN519.H13CORE.0.P.C</td>\n",
+       "      <td>2.609220e-02</td>\n",
+       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>KLF9.H13CORE.1.P.B</td>\n",
+       "      <td>1.561570e-01</td>\n",
+       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>pos_patterns.pattern_19</td>\n",
+       "      <td>21</td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_19.cwm.fwd.png\" width=\"240\" ></td>\n",
+       "      <td><img src=\"trimmed_logos/pos_patterns.pattern_19.cwm.rev.png\" width=\"240\" ></td>\n",
+       "      <td>ZNF70.H13CORE.0.PSG.A</td>\n",
+       "      <td>9.806130e-01</td>\n",
+       "      <td><img src=\"ZNF70.H13CORE.0.PSG.A.png\" width=\"240\" ></td>\n",
+       "      <td>ESX1.H13CORE.1.S.B</td>\n",
+       "      <td>9.806130e-01</td>\n",
+       "      <td><img src=\"ESX1.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
+       "      <td>ALX3.H13CORE.0.SM.B</td>\n",
+       "      <td>9.806130e-01</td>\n",
+       "      <td><img src=\"ALX3.H13CORE.0.SM.B.png\" width=\"240\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <td>neg_patterns.pattern_0</td>\n",
-       "      <td>300</td>\n",
+       "      <td>285</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_0.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_0.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>FLI1.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.002008</td>\n",
-       "      <td><img src=\"FLI1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
-       "      <td>ELK4.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.002008</td>\n",
-       "      <td><img src=\"ELK4.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
-       "      <td>FEV.H13CORE.0.S.B</td>\n",
-       "      <td>0.002008</td>\n",
-       "      <td><img src=\"FEV.H13CORE.0.S.B.png\" width=\"240\" ></td>\n",
+       "      <td>ERG.H13CORE.0.P.B</td>\n",
+       "      <td>1.806400e-03</td>\n",
+       "      <td><img src=\"ERG.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ETS2.H13CORE.1.P.B</td>\n",
+       "      <td>1.806400e-03</td>\n",
+       "      <td><img src=\"ETS2.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ETV6.H13CORE.0.PS.A</td>\n",
+       "      <td>2.267910e-03</td>\n",
+       "      <td><img src=\"ETV6.H13CORE.0.PS.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_1</td>\n",
-       "      <td>110</td>\n",
+       "      <td>136</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_1.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_1.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>MYT1L.H13CORE.0.P.C</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"MYT1L.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
-       "      <td>MSANTD4.H13CORE.0.SGIB.A</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"MSANTD4.H13CORE.0.SGIB.A.png\" width=\"240\" ></td>\n",
-       "      <td>NR1I3.H13CORE.1.PSM.A</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"NR1I3.H13CORE.1.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>ZEB1.H13CORE.0.P.B</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZEB1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN616.H13CORE.0.P.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZN616.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>ZKSC5.H13CORE.0.P.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZKSC5.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_2</td>\n",
-       "      <td>109</td>\n",
+       "      <td>122</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_2.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_2.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ZN519.H13CORE.0.P.C</td>\n",
-       "      <td>0.003585</td>\n",
-       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
-       "      <td>KLF9.H13CORE.1.P.B</td>\n",
-       "      <td>0.005799</td>\n",
-       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN362.H13CORE.0.P.C</td>\n",
+       "      <td>2.168340e-06</td>\n",
+       "      <td><img src=\"ZN362.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>CPEB1.H13CORE.1.S.B</td>\n",
+       "      <td>6.229750e-04</td>\n",
+       "      <td><img src=\"CPEB1.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN613.H13CORE.0.P.C</td>\n",
+       "      <td>1.265990e-03</td>\n",
+       "      <td><img src=\"ZN613.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_3</td>\n",
-       "      <td>108</td>\n",
+       "      <td>104</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_3.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_3.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>SATB1.H13CORE.0.P.B</td>\n",
-       "      <td>0.001747</td>\n",
-       "      <td><img src=\"SATB1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>LMX1B.H13CORE.2.S.B</td>\n",
-       "      <td>0.032521</td>\n",
-       "      <td><img src=\"LMX1B.H13CORE.2.S.B.png\" width=\"240\" ></td>\n",
-       "      <td>ZNF618.H13CORE.0.G.B</td>\n",
-       "      <td>0.037035</td>\n",
-       "      <td><img src=\"ZNF618.H13CORE.0.G.B.png\" width=\"240\" ></td>\n",
+       "      <td>MSANTD4.H13CORE.0.SGIB.A</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"MSANTD4.H13CORE.0.SGIB.A.png\" width=\"240\" ></td>\n",
+       "      <td>MYT1L.H13CORE.0.P.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"MYT1L.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>MYT1.H13CORE.0.SG.A</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"MYT1.H13CORE.0.SG.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_4</td>\n",
-       "      <td>74</td>\n",
+       "      <td>91</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_4.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_4.cwm.rev.png\" width=\"240\" ></td>\n",
        "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.129437</td>\n",
+       "      <td>3.907660e-05</td>\n",
        "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>CREB1.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.142209</td>\n",
-       "      <td><img src=\"CREB1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
        "      <td>SP2.H13CORE.1.P.B</td>\n",
-       "      <td>0.153718</td>\n",
+       "      <td>4.158810e-04</td>\n",
        "      <td><img src=\"SP2.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>SP3.H13CORE.0.P.B</td>\n",
+       "      <td>4.158810e-04</td>\n",
+       "      <td><img src=\"SP3.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_5</td>\n",
-       "      <td>73</td>\n",
+       "      <td>74</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_5.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_5.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "      <td>KLF9.H13CORE.1.P.B</td>\n",
-       "      <td>0.000002</td>\n",
+       "      <td>1.066410e-04</td>\n",
        "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>PATZ1.H13CORE.0.P.B</td>\n",
-       "      <td>0.000023</td>\n",
-       "      <td><img src=\"PATZ1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
+       "      <td>1.094160e-04</td>\n",
+       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>KLF16.H13CORE.1.P.B</td>\n",
+       "      <td>1.094160e-04</td>\n",
+       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_6</td>\n",
-       "      <td>73</td>\n",
+       "      <td>72</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_6.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_6.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZEB1.H13CORE.0.P.B</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"ZEB1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ZN616.H13CORE.0.P.C</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"ZN616.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
-       "      <td>ZN778.H13CORE.1.P.B</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"ZN778.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZBT14.H13CORE.0.P.C</td>\n",
+       "      <td>4.462310e-01</td>\n",
+       "      <td><img src=\"ZBT14.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>ZN454.H13CORE.1.SM.B</td>\n",
+       "      <td>4.649430e-01</td>\n",
+       "      <td><img src=\"ZN454.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
+       "      <td>GMEB2.H13CORE.0.P.C</td>\n",
+       "      <td>4.649430e-01</td>\n",
+       "      <td><img src=\"GMEB2.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_7</td>\n",
-       "      <td>70</td>\n",
+       "      <td>72</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_7.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_7.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>IRF8.H13CORE.0.P.B</td>\n",
-       "      <td>0.014792</td>\n",
-       "      <td><img src=\"IRF8.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>IRF4.H13CORE.0.P.B</td>\n",
-       "      <td>0.076993</td>\n",
-       "      <td><img src=\"IRF4.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>LMX1B.H13CORE.0.P.C</td>\n",
-       "      <td>0.076993</td>\n",
-       "      <td><img src=\"LMX1B.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>ZN432.H13CORE.0.P.C</td>\n",
+       "      <td>1.755440e-01</td>\n",
+       "      <td><img src=\"ZN432.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>SATB1.H13CORE.0.P.B</td>\n",
+       "      <td>1.755440e-01</td>\n",
+       "      <td><img src=\"SATB1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZBT40.H13CORE.0.P.B</td>\n",
+       "      <td>2.858940e-01</td>\n",
+       "      <td><img src=\"ZBT40.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_8</td>\n",
-       "      <td>66</td>\n",
+       "      <td>63</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_8.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_8.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>PO4F2.H13CORE.0.S.B</td>\n",
-       "      <td>0.147974</td>\n",
-       "      <td><img src=\"PO4F2.H13CORE.0.S.B.png\" width=\"240\" ></td>\n",
-       "      <td>PO3F2.H13CORE.1.S.B</td>\n",
-       "      <td>0.354977</td>\n",
-       "      <td><img src=\"PO3F2.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
-       "      <td>PAX4.H13CORE.1.S.C</td>\n",
-       "      <td>0.354977</td>\n",
-       "      <td><img src=\"PAX4.H13CORE.1.S.C.png\" width=\"240\" ></td>\n",
+       "      <td>STAT2.H13CORE.0.P.B</td>\n",
+       "      <td>1.304070e-03</td>\n",
+       "      <td><img src=\"STAT2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>STAT1.H13CORE.1.P.B</td>\n",
+       "      <td>4.996890e-03</td>\n",
+       "      <td><img src=\"STAT1.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>IRF1.H13CORE.0.P.B</td>\n",
+       "      <td>9.939080e-03</td>\n",
+       "      <td><img src=\"IRF1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_9</td>\n",
-       "      <td>61</td>\n",
+       "      <td>60</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_9.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_9.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.002038</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>KLF9.H13CORE.1.P.B</td>\n",
+       "      <td>6.989590e-14</td>\n",
+       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "      <td>SP2.H13CORE.1.P.B</td>\n",
-       "      <td>0.013105</td>\n",
+       "      <td>5.110540e-13</td>\n",
        "      <td><img src=\"SP2.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>MYSM1.H13CORE.0.P.B</td>\n",
-       "      <td>0.013105</td>\n",
-       "      <td><img src=\"MYSM1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>KLF16.H13CORE.1.P.B</td>\n",
+       "      <td>3.646010e-12</td>\n",
+       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_10</td>\n",
-       "      <td>60</td>\n",
+       "      <td>58</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_10.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_10.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZN784.H13CORE.0.SM.B</td>\n",
-       "      <td>0.027715</td>\n",
-       "      <td><img src=\"ZN784.H13CORE.0.SM.B.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.139139</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>PATZ1.H13CORE.0.P.B</td>\n",
-       "      <td>0.152445</td>\n",
-       "      <td><img src=\"PATZ1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>TYY1.H13CORE.0.PSM.A</td>\n",
+       "      <td>2.596370e-03</td>\n",
+       "      <td><img src=\"TYY1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>TYY2.H13CORE.1.SM.B</td>\n",
+       "      <td>2.596370e-03</td>\n",
+       "      <td><img src=\"TYY2.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
+       "      <td>TYY2.H13CORE.0.PS.A</td>\n",
+       "      <td>2.596370e-03</td>\n",
+       "      <td><img src=\"TYY2.H13CORE.0.PS.A.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_11</td>\n",
-       "      <td>52</td>\n",
+       "      <td>57</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_11.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_11.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>STAT2.H13CORE.0.P.B</td>\n",
-       "      <td>0.000388</td>\n",
-       "      <td><img src=\"STAT2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>STAT1.H13CORE.1.P.B</td>\n",
-       "      <td>0.001192</td>\n",
-       "      <td><img src=\"STAT1.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>IRF4.H13CORE.0.P.B</td>\n",
-       "      <td>0.037773</td>\n",
-       "      <td><img src=\"IRF4.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN266.H13CORE.0.P.B</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZN266.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>NR1I2.H13CORE.1.S.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"NR1I2.H13CORE.1.S.C.png\" width=\"240\" ></td>\n",
+       "      <td>NR1I2.H13CORE.0.M.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"NR1I2.H13CORE.0.M.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_12</td>\n",
-       "      <td>51</td>\n",
+       "      <td>49</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_12.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_12.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>CREB1.H13CORE.0.PSM.A</td>\n",
-       "      <td>0.200907</td>\n",
-       "      <td><img src=\"CREB1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
-       "      <td>SP140L.H13CORE.0.PSGIB.A</td>\n",
-       "      <td>0.265513</td>\n",
-       "      <td><img src=\"SP140L.H13CORE.0.PSGIB.A.png\" width=\"240\" ></td>\n",
-       "      <td>SRBP1.H13CORE.0.P.B</td>\n",
-       "      <td>0.265513</td>\n",
-       "      <td><img src=\"SRBP1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>SIX2.H13CORE.0.P.B</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"SIX2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>SIX1.H13CORE.0.P.B</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"SIX1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN254.H13CORE.0.P.C</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td><img src=\"ZN254.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_13</td>\n",
-       "      <td>46</td>\n",
+       "      <td>47</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_13.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_13.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>MYT1L.H13CORE.0.P.C</td>\n",
-       "      <td>0.034112</td>\n",
-       "      <td><img src=\"MYT1L.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
-       "      <td>Z354A.H13CORE.0.P.B</td>\n",
-       "      <td>0.558669</td>\n",
-       "      <td><img src=\"Z354A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>ZNF618.H13CORE.0.G.B</td>\n",
-       "      <td>0.736356</td>\n",
-       "      <td><img src=\"ZNF618.H13CORE.0.G.B.png\" width=\"240\" ></td>\n",
+       "      <td>HSF1.H13CORE.1.P.B</td>\n",
+       "      <td>4.076720e-01</td>\n",
+       "      <td><img src=\"HSF1.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>NFAC3.H13CORE.1.P.C</td>\n",
+       "      <td>4.076720e-01</td>\n",
+       "      <td><img src=\"NFAC3.H13CORE.1.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>RUNX3.H13CORE.1.S.B</td>\n",
+       "      <td>8.661130e-01</td>\n",
+       "      <td><img src=\"RUNX3.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_14</td>\n",
-       "      <td>45</td>\n",
+       "      <td>44</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_14.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_14.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>SP2.H13CORE.1.P.B</td>\n",
-       "      <td>0.000071</td>\n",
-       "      <td><img src=\"SP2.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>KLF9.H13CORE.1.P.B</td>\n",
-       "      <td>0.000304</td>\n",
-       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>KLF16.H13CORE.1.P.B</td>\n",
-       "      <td>0.000304</td>\n",
-       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>NFAC1.H13CORE.0.P.B</td>\n",
+       "      <td>3.999920e-05</td>\n",
+       "      <td><img src=\"NFAC1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>NFE2.H13CORE.1.SM.B</td>\n",
+       "      <td>2.837520e-04</td>\n",
+       "      <td><img src=\"NFE2.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
+       "      <td>BACH2.H13CORE.1.SM.B</td>\n",
+       "      <td>4.137020e-04</td>\n",
+       "      <td><img src=\"BACH2.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_15</td>\n",
-       "      <td>41</td>\n",
+       "      <td>43</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_15.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_15.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>ZN266.H13CORE.0.P.B</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"ZN266.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>RORB.H13CORE.0.SM.B</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"RORB.H13CORE.0.SM.B.png\" width=\"240\" ></td>\n",
-       "      <td>NR1I2.H13CORE.1.S.C</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td><img src=\"NR1I2.H13CORE.1.S.C.png\" width=\"240\" ></td>\n",
+       "      <td>SPIB.H13CORE.0.P.B</td>\n",
+       "      <td>2.458950e-01</td>\n",
+       "      <td><img src=\"SPIB.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>IRF8.H13CORE.0.P.B</td>\n",
+       "      <td>2.458950e-01</td>\n",
+       "      <td><img src=\"IRF8.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>IRF4.H13CORE.1.S.B</td>\n",
+       "      <td>4.113850e-01</td>\n",
+       "      <td><img src=\"IRF4.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_16</td>\n",
-       "      <td>40</td>\n",
+       "      <td>38</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_16.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_16.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.013985</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>KLF16.H13CORE.1.P.B</td>\n",
-       "      <td>0.020838</td>\n",
-       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>PATZ1.H13CORE.0.P.B</td>\n",
-       "      <td>0.022392</td>\n",
-       "      <td><img src=\"PATZ1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>CREB1.H13CORE.0.PSM.A</td>\n",
+       "      <td>1.453930e-02</td>\n",
+       "      <td><img src=\"CREB1.H13CORE.0.PSM.A.png\" width=\"240\" ></td>\n",
+       "      <td>ATF1.H13CORE.0.P.B</td>\n",
+       "      <td>1.453930e-02</td>\n",
+       "      <td><img src=\"ATF1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ATF1.H13CORE.1.P.B</td>\n",
+       "      <td>1.453930e-02</td>\n",
+       "      <td><img src=\"ATF1.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_17</td>\n",
-       "      <td>38</td>\n",
+       "      <td>35</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_17.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_17.cwm.rev.png\" width=\"240\" ></td>\n",
        "      <td>TAF1.H13CORE.0.P.B</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>7.852060e-06</td>\n",
        "      <td><img src=\"TAF1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
        "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.000009</td>\n",
+       "      <td>3.472120e-03</td>\n",
        "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>TYY2.H13CORE.0.PS.A</td>\n",
-       "      <td>0.000059</td>\n",
-       "      <td><img src=\"TYY2.H13CORE.0.PS.A.png\" width=\"240\" ></td>\n",
+       "      <td>KLF16.H13CORE.1.P.B</td>\n",
+       "      <td>3.472120e-03</td>\n",
+       "      <td><img src=\"KLF16.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_18</td>\n",
-       "      <td>38</td>\n",
+       "      <td>29</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_18.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_18.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
-       "      <td>0.000109</td>\n",
-       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>KLF9.H13CORE.1.P.B</td>\n",
-       "      <td>0.007919</td>\n",
-       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>PATZ1.H13CORE.0.P.B</td>\n",
-       "      <td>0.021664</td>\n",
-       "      <td><img src=\"PATZ1.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>TFEC.H13CORE.0.M.B</td>\n",
+       "      <td>5.423550e-01</td>\n",
+       "      <td><img src=\"TFEC.H13CORE.0.M.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN449.H13CORE.1.S.C</td>\n",
+       "      <td>5.423550e-01</td>\n",
+       "      <td><img src=\"ZN449.H13CORE.1.S.C.png\" width=\"240\" ></td>\n",
+       "      <td>GLIS3.H13CORE.1.S.C</td>\n",
+       "      <td>5.423550e-01</td>\n",
+       "      <td><img src=\"GLIS3.H13CORE.1.S.C.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>neg_patterns.pattern_19</td>\n",
-       "      <td>37</td>\n",
+       "      <td>26</td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_19.cwm.fwd.png\" width=\"240\" ></td>\n",
        "      <td><img src=\"trimmed_logos/neg_patterns.pattern_19.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>NFE2.H13CORE.1.SM.B</td>\n",
-       "      <td>0.001095</td>\n",
-       "      <td><img src=\"NFE2.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
-       "      <td>NFAC2.H13CORE.0.P.B</td>\n",
-       "      <td>0.001095</td>\n",
-       "      <td><img src=\"NFAC2.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>BACH2.H13CORE.1.SM.B</td>\n",
-       "      <td>0.001095</td>\n",
-       "      <td><img src=\"BACH2.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>neg_patterns.pattern_20</td>\n",
-       "      <td>33</td>\n",
-       "      <td><img src=\"trimmed_logos/neg_patterns.pattern_20.cwm.fwd.png\" width=\"240\" ></td>\n",
-       "      <td><img src=\"trimmed_logos/neg_patterns.pattern_20.cwm.rev.png\" width=\"240\" ></td>\n",
-       "      <td>IRF4.H13CORE.1.S.B</td>\n",
-       "      <td>0.489419</td>\n",
-       "      <td><img src=\"IRF4.H13CORE.1.S.B.png\" width=\"240\" ></td>\n",
-       "      <td>IRF8.H13CORE.0.P.B</td>\n",
-       "      <td>0.489419</td>\n",
-       "      <td><img src=\"IRF8.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
-       "      <td>IRF7.H13CORE.1.SM.B</td>\n",
-       "      <td>0.489419</td>\n",
-       "      <td><img src=\"IRF7.H13CORE.1.SM.B.png\" width=\"240\" ></td>\n",
+       "      <td>KMT2A.H13CORE.0.P.B</td>\n",
+       "      <td>3.903350e-07</td>\n",
+       "      <td><img src=\"KMT2A.H13CORE.0.P.B.png\" width=\"240\" ></td>\n",
+       "      <td>ZN519.H13CORE.0.P.C</td>\n",
+       "      <td>8.733140e-05</td>\n",
+       "      <td><img src=\"ZN519.H13CORE.0.P.C.png\" width=\"240\" ></td>\n",
+       "      <td>KLF9.H13CORE.1.P.B</td>\n",
+       "      <td>8.134530e-04</td>\n",
+       "      <td><img src=\"KLF9.H13CORE.1.P.B.png\" width=\"240\" ></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -1350,25 +1361,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "41a34957",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:10:07.805193Z",
-     "iopub.status.busy": "2025-11-21T06:10:07.805063Z",
-     "iopub.status.idle": "2025-11-21T06:10:07.866956Z",
-     "shell.execute_reply": "2025-11-21T06:10:07.865904Z"
-    }
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1418,16 +1424,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "3b3faf06",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:10:07.868676Z",
-     "iopub.status.busy": "2025-11-21T06:10:07.868508Z",
-     "iopub.status.idle": "2025-11-21T06:11:28.538893Z",
-     "shell.execute_reply": "2025-11-21T06:11:28.538153Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1435,21 +1434,13 @@
      "text": [
       "decima - INFO - Using device: 0\n",
       "decima - INFO - Loading model v1_rep0 and metadata to compute attributions...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:00.8 (863.9MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m A graphql request initiated by the public wandb API timed out (timeout=19 sec). Create a new API with an integer timeout larger than 19, e.g., `api = wandb.Api(timeout=29)` to increase the graphql timeout.\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.6 (1964.2MB/s)\n",
-      "/home/celikm5/Projects/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
-      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 330.03it/s]\n",
-      "Computing attributions...:   8%|█▎               | 4/50 [00:04<00:44,  1.03it/s]decima - WARNING - Gene XRCC5 has low correlation with the model. Pearson: 0.4621304058925145. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  10%|█▋               | 5/50 [00:05<00:41,  1.08it/s]decima - WARNING - Gene FNIP1 has low correlation with the model. Pearson: 0.044592478397006044. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  40%|██████▍         | 20/50 [00:17<00:25,  1.19it/s]decima - WARNING - Gene LRRFIP1 has low correlation with the model. Pearson: 0.4621130932978845. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  70%|███████████▏    | 35/50 [00:30<00:12,  1.20it/s]decima - WARNING - Gene CHD4 has low correlation with the model. Pearson: 0.27165706153002717. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...: 100%|████████████████| 50/50 [00:42<00:00,  1.17it/s]\n",
+      "/home/lala8/code/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 193.06it/s]\n",
+      "Computing attributions...:   8%|█▎               | 4/50 [00:05<00:54,  1.19s/it]decima - WARNING - Gene XRCC5 has low correlation with the model. Pearson: 0.4621304058925145. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  10%|█▋               | 5/50 [00:06<00:50,  1.13s/it]decima - WARNING - Gene FNIP1 has low correlation with the model. Pearson: 0.044592478397006044. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  40%|██████▍         | 20/50 [00:21<00:29,  1.02it/s]decima - WARNING - Gene LRRFIP1 has low correlation with the model. Pearson: 0.4621130932978845. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  70%|███████████▏    | 35/50 [00:35<00:14,  1.05it/s]decima - WARNING - Gene CHD4 has low correlation with the model. Pearson: 0.27165706153002717. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...: 100%|████████████████| 50/50 [00:50<00:00,  1.00s/it]\n",
       "\u001b[0m"
      ]
     }
@@ -1468,21 +1459,14 @@
    "id": "bc622b7f",
    "metadata": {},
    "source": [
-    "end second replicate:"
+    "and the second replicate:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "dffdc854",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:11:28.541730Z",
-     "iopub.status.busy": "2025-11-21T06:11:28.541459Z",
-     "iopub.status.idle": "2025-11-21T06:13:02.045520Z",
-     "shell.execute_reply": "2025-11-21T06:13:02.044815Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1490,20 +1474,14 @@
      "text": [
       "decima - INFO - Using device: 0\n",
       "decima - INFO - Loading model v1_rep1 and metadata to compute attributions...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep1:latest', 720.03MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:02.0 (361.9MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.7 (1872.3MB/s)\n",
-      "/home/celikm5/Projects/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
-      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 321.99it/s]\n",
-      "Computing attributions...:   8%|█▎               | 4/50 [00:04<00:43,  1.06it/s]decima - WARNING - Gene XRCC5 has low correlation with the model. Pearson: 0.4621304058925145. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  10%|█▋               | 5/50 [00:04<00:40,  1.10it/s]decima - WARNING - Gene FNIP1 has low correlation with the model. Pearson: 0.044592478397006044. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  40%|██████▍         | 20/50 [00:17<00:25,  1.19it/s]decima - WARNING - Gene LRRFIP1 has low correlation with the model. Pearson: 0.4621130932978845. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...:  70%|███████████▏    | 35/50 [00:30<00:12,  1.19it/s]decima - WARNING - Gene CHD4 has low correlation with the model. Pearson: 0.27165706153002717. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
-      "Computing attributions...: 100%|████████████████| 50/50 [00:42<00:00,  1.17it/s]\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "/home/lala8/code/decima/src/decima/interpret/attributer.py:66: UserWarning: `off_tasks` is not provided. Using all other tasks as off_tasks.\n",
+      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 198.97it/s]\n",
+      "Computing attributions...:   8%|█▎               | 4/50 [00:05<00:54,  1.18s/it]decima - WARNING - Gene XRCC5 has low correlation with the model. Pearson: 0.4621304058925145. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  10%|█▋               | 5/50 [00:06<00:49,  1.10s/it]decima - WARNING - Gene FNIP1 has low correlation with the model. Pearson: 0.044592478397006044. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  40%|██████▍         | 20/50 [00:20<00:28,  1.07it/s]decima - WARNING - Gene LRRFIP1 has low correlation with the model. Pearson: 0.4621130932978845. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...:  70%|███████████▏    | 35/50 [00:35<00:14,  1.03it/s]decima - WARNING - Gene CHD4 has low correlation with the model. Pearson: 0.27165706153002717. Be careful with the predictions of the model for this gene. Check `DecimaResult.load().gene_metadata['pearson']` to see the correlation of the gene with the model.\n",
+      "Computing attributions...: 100%|████████████████| 50/50 [00:49<00:00,  1.01it/s]\n",
       "\u001b[0m"
      ]
     }
@@ -1519,16 +1497,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "id": "a9808dc8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:13:02.047298Z",
-     "iopub.status.busy": "2025-11-21T06:13:02.047109Z",
-     "iopub.status.idle": "2025-11-21T06:13:02.182109Z",
-     "shell.execute_reply": "2025-11-21T06:13:02.181578Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1544,41 +1515,48 @@
       "example/modisco_subcommands/modisco_neurons.seqlets.bed\n",
       "\n",
       "example/modisco_subcommands/modisco_neurons_report:\n",
-      "AHCTF1.H13CORE.0.B.B.png   NKX61.H13CORE.0.PS.A.png\n",
-      "ATF4.H13CORE.2.SM.B.png    NR1D1.H13CORE.0.P.B.png\n",
-      "BACH1.H13CORE.0.P.B.png    NR1I3.H13CORE.1.PSM.A.png\n",
-      "CEBPG.H13CORE.2.P.B.png    NR2F6.H13CORE.3.SM.B.png\n",
-      "CREB1.H13CORE.0.PSM.A.png  PATZ1.H13CORE.0.P.B.png\n",
-      "DDIT3.H13CORE.0.P.B.png    PO5F1.H13CORE.0.P.B.png\n",
-      "ELK4.H13CORE.0.PSM.A.png   PRD13.H13CORE.0.P.B.png\n",
-      "ERR1.H13CORE.0.PSM.A.png   RORG.H13CORE.0.M.C.png\n",
-      "ERR2.H13CORE.0.PSM.A.png   SNAI2.H13CORE.0.PSM.A.png\n",
-      "ERR3.H13CORE.0.PSM.A.png   SOX2.H13CORE.1.P.B.png\n",
-      "ETV4.H13CORE.0.P.B.png\t   SOX3.H13CORE.1.S.C.png\n",
-      "ETV6.H13CORE.0.PS.A.png    SP140L.H13CORE.0.PSGIB.A.png\n",
-      "FEV.H13CORE.0.S.B.png\t   SP2.H13CORE.1.P.B.png\n",
-      "FOSB.H13CORE.1.M.C.png\t   SP3.H13CORE.0.P.B.png\n",
-      "FOS.H13CORE.1.S.C.png\t   SPIB.H13CORE.0.P.B.png\n",
-      "HTF4.H13CORE.0.PSM.A.png   SPIB.H13CORE.1.S.C.png\n",
-      "IRF1.H13CORE.0.P.B.png\t   STAT2.H13CORE.0.P.B.png\n",
-      "IRF4.H13CORE.0.P.B.png\t   TAF1.H13CORE.0.P.B.png\n",
-      "IRF8.H13CORE.0.P.B.png\t   TFE3.H13CORE.0.PSM.A.png\n",
-      "ITF2.H13CORE.1.PSM.A.png   TFEB.H13CORE.0.SM.B.png\n",
-      "JDP2.H13CORE.0.SM.B.png    TFEC.H13CORE.0.M.B.png\n",
-      "JUN.H13CORE.0.P.B.png\t   TFEC.H13CORE.1.SM.B.png\n",
-      "KLF12.H13CORE.0.P.C.png    trimmed_logos\n",
-      "KLF16.H13CORE.1.P.B.png    TYY1.H13CORE.0.PSM.A.png\n",
-      "KLF8.H13CORE.1.P.C.png\t   TYY2.H13CORE.0.PS.A.png\n",
-      "KLF9.H13CORE.1.P.B.png\t   ZBT14.H13CORE.0.P.C.png\n",
-      "KMT2A.H13CORE.0.P.B.png    ZEB1.H13CORE.0.P.B.png\n",
-      "MESP1.H13CORE.0.S.C.png    ZEB2.H13CORE.0.P.B.png\n",
-      "motifs.html\t\t   ZFP28.H13CORE.0.P.B.png\n",
-      "MYC.H13CORE.0.P.B.png\t   ZN37A.H13CORE.0.P.B.png\n",
-      "MYCN.H13CORE.0.PS.A.png    ZN519.H13CORE.0.P.C.png\n",
-      "MYT1.H13CORE.0.SG.A.png    ZN543.H13CORE.0.P.C.png\n",
-      "MYT1L.H13CORE.0.P.C.png    ZN616.H13CORE.0.P.C.png\n",
-      "NANOG.H13CORE.0.P.B.png    ZN778.H13CORE.1.P.B.png\n",
-      "NF2L2.H13CORE.0.P.B.png    ZNF618.H13CORE.0.G.B.png\n"
+      "ATF6A.H13CORE.0.SM.B.png      ONEC2.H13CORE.3.P.B.png\n",
+      "BACH1.H13CORE.0.P.B.png       PKNX1.H13CORE.3.SM.B.png\n",
+      "BATF2.H13CORE.0.PSG.A.png     RFX1.H13CORE.0.PM.A.png\n",
+      "BHA15.H13CORE.0.P.B.png       RFX3.H13CORE.0.PSM.A.png\n",
+      "CREB1.H13CORE.0.PSM.A.png     SATB1.H13CORE.0.P.B.png\n",
+      "DMRT3.H13CORE.0.S.C.png       SKOR1.H13CORE.1.S.C.png\n",
+      "E2F8.H13CORE.1.SM.B.png       SNAI2.H13CORE.0.PSM.A.png\n",
+      "EHF.H13CORE.0.P.B.png\t      SP2.H13CORE.1.P.B.png\n",
+      "EOMES.H13CORE.0.PSM.A.png     SP3.H13CORE.0.P.B.png\n",
+      "ERR3.H13CORE.0.PSM.A.png      SPIB.H13CORE.1.S.C.png\n",
+      "ETV2.H13CORE.0.S.B.png\t      STAT1.H13CORE.1.P.B.png\n",
+      "ETV4.H13CORE.0.P.B.png\t      STAT2.H13CORE.0.P.B.png\n",
+      "ETV5.H13CORE.1.PM.A.png       TAF1.H13CORE.0.P.B.png\n",
+      "ETV6.H13CORE.0.PS.A.png       TFE2.H13CORE.0.PS.A.png\n",
+      "ETV7.H13CORE.0.SM.B.png       trimmed_logos\n",
+      "FBXL19.H13CORE.0.PSGIB.A.png  TYY1.H13CORE.0.PSM.A.png\n",
+      "FEV.H13CORE.0.S.B.png\t      TYY1.H13CORE.1.S.B.png\n",
+      "FOSB.H13CORE.0.P.B.png\t      TYY2.H13CORE.0.PS.A.png\n",
+      "GABPA.H13CORE.0.PSM.A.png     TYY2.H13CORE.1.SM.B.png\n",
+      "HTF4.H13CORE.0.PSM.A.png      ZBTB40.H13CORE.0.PSG.A.png\n",
+      "HXD11.H13CORE.0.PS.A.png      ZEB1.H13CORE.0.P.B.png\n",
+      "IRF3.H13CORE.1.SM.B.png       ZEB2.H13CORE.0.P.B.png\n",
+      "IRF8.H13CORE.0.P.B.png\t      ZFP42.H13CORE.0.PM.A.png\n",
+      "ITF2.H13CORE.1.PSM.A.png      ZFX.H13CORE.0.P.B.png\n",
+      "KLF13.H13CORE.1.P.C.png       ZKSC5.H13CORE.0.P.C.png\n",
+      "KLF16.H13CORE.1.P.B.png       ZN362.H13CORE.0.P.C.png\n",
+      "KLF9.H13CORE.1.P.B.png\t      ZN454.H13CORE.1.SM.B.png\n",
+      "KMT2A.H13CORE.0.P.B.png       ZN483.H13CORE.0.P.C.png\n",
+      "KMT2B.H13CORE.0.P.B.png       ZN510.H13CORE.0.P.C.png\n",
+      "LHX1.H13CORE.1.M.C.png\t      ZN519.H13CORE.0.P.C.png\n",
+      "MAD4.H13CORE.0.P.C.png\t      ZN554.H13CORE.1.P.C.png\n",
+      "MAZ.H13CORE.0.P.B.png\t      ZN584.H13CORE.1.P.C.png\n",
+      "MNT.H13CORE.0.PS.A.png\t      ZN595.H13CORE.0.P.C.png\n",
+      "motifs.html\t\t      ZN616.H13CORE.0.P.C.png\n",
+      "MYC.H13CORE.0.P.B.png\t      ZN716.H13CORE.0.P.C.png\n",
+      "MYSM1.H13CORE.0.P.B.png       ZN778.H13CORE.1.P.B.png\n",
+      "MYT1.H13CORE.0.SG.A.png       ZN784.H13CORE.0.SM.B.png\n",
+      "MYT1L.H13CORE.0.P.C.png       ZNF35.H13CORE.0.P.C.png\n",
+      "NF2L1.H13CORE.0.P.B.png       ZNF575.H13CORE.0.PSG.A.png\n",
+      "NF2L2.H13CORE.0.P.B.png       ZNF618.H13CORE.0.G.B.png\n",
+      "NFAC1.H13CORE.0.P.B.png       ZNF79.H13CORE.0.P.C.png\n",
+      "NR5A2.H13CORE.0.PSM.A.png\n"
      ]
     }
    ],
@@ -1599,214 +1577,204 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "025b6519",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:13:02.183509Z",
-     "iopub.status.busy": "2025-11-21T06:13:02.183359Z",
-     "iopub.status.idle": "2025-11-21T06:13:40.220609Z",
-     "shell.execute_reply": "2025-11-21T06:13:40.219968Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "decima - INFO - Loading metadata for model ensemble...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:02.0 (1586.1MB/s)\n",
-      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 364.41it/s]\n",
-      "Loading attributions and sequences...: 100%|████| 50/50 [00:10<00:00,  4.87it/s]\n",
-      "2025-11-25 12:24:08,734 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
-      "2025-11-25 12:24:08,740 - modisco-lite - INFO - Extracting seqlets for 50 tasks:\n",
-      "2025-11-25 12:24:08,740 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
-      "2025-11-25 12:24:08,753 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
-      "2025-11-25 12:24:08,810 - modisco-lite - INFO - - Computing isotonic thresholds\n",
-      "2025-11-25 12:24:08,841 - modisco-lite - INFO - - Refining thresholds\n",
-      "2025-11-25 12:24:09,134 - modisco-lite - INFO - - Extracting seqlets\n",
-      "2025-11-25 12:24:09,185 - modisco-lite - INFO - - Extracting 1398 positive seqlets\n",
-      "2025-11-25 12:24:09,192 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 1398 seqlets\n",
-      "100%|██████████████████████████████████████| 1398/1398 [00:05<00:00, 238.94it/s]\n",
-      "2025-11-25 12:24:22,828 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 1398 seqlets and 1398 neighbors\n",
-      "100%|██████████████████████████████████████| 1398/1398 [00:01<00:00, 995.30it/s]\n",
-      "100%|██████████████████████████████████████| 1398/1398 [00:01<00:00, 993.73it/s]\n",
-      "2025-11-25 12:24:25,746 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-25 12:24:26,513 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-25 12:24:29,124 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 198.63it/s]\n",
+      "Loading attributions and sequences...: 100%|████| 50/50 [00:10<00:00,  4.95it/s]\n",
+      "2026-05-24 22:26:01,522 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
+      "2026-05-24 22:26:01,530 - modisco-lite - INFO - Extracting seqlets for 50 tasks:\n",
+      "2026-05-24 22:26:01,530 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
+      "2026-05-24 22:26:01,548 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
+      "2026-05-24 22:26:01,626 - modisco-lite - INFO - - Computing isotonic thresholds\n",
+      "2026-05-24 22:26:01,666 - modisco-lite - INFO - - Refining thresholds\n",
+      "2026-05-24 22:26:02,040 - modisco-lite - INFO - - Extracting seqlets\n",
+      "2026-05-24 22:26:02,105 - modisco-lite - INFO - - Extracting 1395 positive seqlets\n",
+      "2026-05-24 22:26:02,114 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 1395 seqlets\n",
+      "100%|██████████████████████████████████████| 1395/1395 [00:06<00:00, 231.72it/s]\n",
+      "2026-05-24 22:26:16,545 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 1395 seqlets and 1395 neighbors\n",
+      "100%|██████████████████████████████████████| 1395/1395 [00:01<00:00, 807.90it/s]\n",
+      "100%|██████████████████████████████████████| 1395/1395 [00:01<00:00, 807.84it/s]\n",
+      "2026-05-24 22:26:20,128 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:26:21,010 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:26:24,589 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
       "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    7.5s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    7.6s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    7.6s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    8.0s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   10.3s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   10.4s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   10.7s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   12.1s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   13.0s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   14.0s remaining:    8.4s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   14.4s remaining:    6.5s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   14.6s remaining:    4.9s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   15.2s remaining:    3.5s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   17.1s remaining:    2.4s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   17.3s finished\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.19402832229401765\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.1940264791321347\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.19402832229401765\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.19402832229401765\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.1940091525623618\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.19403347967609325\n",
-      "2025-11-25 12:24:46,515 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.19402752710578844\n",
-      "2025-11-25 12:24:46,518 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.79it/s]\n",
-      "2025-11-25 12:24:47,596 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1253 seqlets\n",
-      "100%|██████████████████████████████████████| 1253/1253 [00:01<00:00, 755.96it/s]\n",
-      "2025-11-25 12:24:49,789 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1253 seqlets and 1253 neighbors\n",
-      "100%|██████████████████████████████████████| 1253/1253 [00:03<00:00, 372.76it/s]\n",
-      "100%|██████████████████████████████████████| 1253/1253 [00:03<00:00, 372.43it/s]\n",
-      "2025-11-25 12:24:56,623 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-25 12:24:59,143 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    8.1s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    8.3s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    8.5s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    8.8s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   11.5s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   12.0s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   12.4s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   12.6s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   14.9s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   15.3s remaining:    9.2s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   15.4s remaining:    7.0s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   16.1s remaining:    5.4s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   18.3s remaining:    4.2s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   18.8s remaining:    2.7s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   19.1s finished\n",
+      "2026-05-24 22:26:43,828 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.1950802892686312\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.1950802892686312\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.1950802892686312\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.1950802892686312\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.1950802892686312\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,829 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.19508711845462268\n",
+      "2026-05-24 22:26:43,833 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.53it/s]\n",
+      "2026-05-24 22:26:45,019 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1249 seqlets\n",
+      "100%|██████████████████████████████████████| 1249/1249 [00:01<00:00, 651.79it/s]\n",
+      "2026-05-24 22:26:47,567 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1249 seqlets and 1249 neighbors\n",
+      "100%|██████████████████████████████████████| 1249/1249 [00:04<00:00, 302.05it/s]\n",
+      "100%|██████████████████████████████████████| 1249/1249 [00:04<00:00, 302.49it/s]\n",
+      "2026-05-24 22:26:55,975 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:26:59,497 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    2.1s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    2.2s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    2.2s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    2.4s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    4.5s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    4.5s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    5.1s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    5.7s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    6.6s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    6.7s remaining:    4.0s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    7.6s remaining:    3.4s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:    9.0s remaining:    3.0s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    9.3s remaining:    2.1s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    9.3s remaining:    1.3s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   11.7s finished\n",
-      "2025-11-25 12:25:10,915 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.16119281732703258\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.16119281732703258\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.16119281732703258\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,916 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.16120846264866814\n",
-      "2025-11-25 12:25:10,918 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.22it/s]\n",
-      "2025-11-25 12:25:12,270 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|████| 3/3 [00:15<00:00,  5.21s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
-      "2025-11-25 12:25:34,606 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|███████████████████| 24/24 [00:00<00:00, 19489.51it/s]\n",
-      "Computing subpatterns:: 100%|███████████████████| 19/19 [00:01<00:00, 11.24it/s]\n",
-      "2025-11-25 12:25:36,298 - modisco-lite - INFO - - Extracting 2246 negative seqlets\n",
-      "2025-11-25 12:25:36,311 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 2246 seqlets\n",
-      "100%|██████████████████████████████████████| 2246/2246 [00:05<00:00, 388.41it/s]\n",
-      "2025-11-25 12:25:44,310 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 2246 seqlets and 2246 neighbors\n",
-      "100%|██████████████████████████████████████| 2246/2246 [00:02<00:00, 987.96it/s]\n",
-      "100%|██████████████████████████████████████| 2246/2246 [00:02<00:00, 993.08it/s]\n",
-      "2025-11-25 12:25:49,012 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-25 12:25:50,256 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-25 12:25:54,700 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
-      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    9.3s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    9.5s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    9.8s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:   10.3s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   15.6s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   15.8s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   15.8s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   16.9s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   22.3s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   22.3s remaining:   13.4s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   22.3s remaining:   10.1s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   22.5s remaining:    7.5s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   27.9s remaining:    6.4s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   28.0s remaining:    4.0s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   32.5s finished\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.2428436201275331\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.24284717240339185\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.24200970784348982\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.24283705042446194\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.24048541352778774\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.24200728159965218\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.24200728159965218\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.24284680775911574\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.2428436201275331\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.24284717240339185\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.24284455097065472\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.24283705042446194\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.24284680775911574\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.24284680775911574\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.24284205599266293\n",
-      "2025-11-25 12:26:27,318 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.24282749294994257\n",
-      "2025-11-25 12:26:27,321 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:01<00:00,  2.06it/s]\n",
-      "2025-11-25 12:26:29,262 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1903 seqlets\n",
-      "100%|██████████████████████████████████████| 1903/1903 [00:03<00:00, 498.08it/s]\n",
-      "2025-11-25 12:26:33,880 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1903 seqlets and 1903 neighbors\n",
-      "100%|██████████████████████████████████████| 1903/1903 [00:05<00:00, 352.61it/s]\n",
-      "100%|██████████████████████████████████████| 1903/1903 [00:05<00:00, 322.64it/s]\n",
-      "2025-11-25 12:26:45,336 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-25 12:26:49,595 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    2.9s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    3.1s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    3.4s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    4.2s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    6.5s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    6.7s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    7.0s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    7.1s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    9.3s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    9.7s remaining:    5.8s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    9.7s remaining:    4.4s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   10.7s remaining:    3.6s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   12.1s remaining:    2.8s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   13.1s remaining:    1.9s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   13.6s finished\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.16449924407175606\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.16449924407175606\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.16449436338832338\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.16449924407175606\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.16449436338832338\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.16448684375646752\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.16449924407175606\n",
+      "2026-05-24 22:27:13,159 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.16449924407175606\n",
+      "2026-05-24 22:27:13,160 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,160 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,160 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.16451016014762165\n",
+      "2026-05-24 22:27:13,160 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 3/3 [00:01<00:00,  2.41it/s]\n",
+      "2026-05-24 22:27:14,408 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|████| 3/3 [00:20<00:00,  6.70s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
+      "2026-05-24 22:27:39,639 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|███████████████████| 21/21 [00:00<00:00, 15175.81it/s]\n",
+      "Computing subpatterns:: 100%|███████████████████| 19/19 [00:55<00:00,  2.90s/it]\n",
+      "2026-05-24 22:28:34,770 - modisco-lite - INFO - - Extracting 2246 negative seqlets\n",
+      "2026-05-24 22:28:34,785 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 2246 seqlets\n",
+      "100%|██████████████████████████████████████| 2246/2246 [00:06<00:00, 335.49it/s]\n",
+      "2026-05-24 22:28:44,064 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 2246 seqlets and 2246 neighbors\n",
+      "100%|██████████████████████████████████████| 2246/2246 [00:02<00:00, 810.48it/s]\n",
+      "100%|██████████████████████████████████████| 2246/2246 [00:02<00:00, 811.14it/s]\n",
+      "2026-05-24 22:28:49,827 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:28:51,267 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:28:57,424 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
+      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   14.6s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:   14.7s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:   16.0s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:   16.4s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   20.2s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   21.2s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   24.0s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   25.3s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   26.1s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   29.8s remaining:   17.9s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   30.2s remaining:   13.7s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   32.1s remaining:   10.7s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   33.9s remaining:    7.8s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   35.6s remaining:    5.1s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   40.9s finished\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.2427148866532598\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.24271757786165873\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.24271680280115834\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.2427109374311804\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.2427093446476442\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.24271778097459995\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.2427122747010229\n",
+      "2026-05-24 22:29:38,479 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.24271757786165873\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.24216198724843893\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.242717558294831\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.2427109374311804\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.24216198724843893\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.24271898535062617\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.24271680280115834\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.24271757786165873\n",
+      "2026-05-24 22:29:38,480 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.24270729098988628\n",
+      "2026-05-24 22:29:38,484 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:02<00:00,  1.93it/s]\n",
+      "2026-05-24 22:29:40,560 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 1901 seqlets\n",
+      "100%|██████████████████████████████████████| 1901/1901 [00:04<00:00, 427.48it/s]\n",
+      "2026-05-24 22:29:45,849 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 1901 seqlets and 1901 neighbors\n",
+      "100%|██████████████████████████████████████| 1901/1901 [00:06<00:00, 301.85it/s]\n",
+      "100%|██████████████████████████████████████| 1901/1901 [00:06<00:00, 302.43it/s]\n",
+      "2026-05-24 22:29:58,678 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:30:04,556 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    3.9s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    4.3s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    5.3s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    5.6s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    7.8s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    9.1s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    9.8s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   11.7s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   14.4s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   15.0s remaining:    9.0s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   17.7s remaining:    8.0s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   17.8s remaining:    5.9s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   20.5s remaining:    4.7s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   21.1s remaining:    3.0s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   23.3s finished\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.1993550018499436\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.1993224566319978\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.19935219556333927\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.19934906079152306\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.199268681627194\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.19934925762555028\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.19935215150301006\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.19926777067757262\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.1993504566639057\n",
-      "2025-11-25 12:27:13,031 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.19935690711758358\n",
-      "2025-11-25 12:27:13,032 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.1993543329214696\n",
-      "2025-11-25 12:27:13,032 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.19934455864873268\n",
-      "2025-11-25 12:27:13,032 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.1992727605561867\n",
-      "2025-11-25 12:27:13,032 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.19934723883369546\n",
-      "2025-11-25 12:27:13,032 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.19934906079152306\n",
-      "2025-11-25 12:27:13,032 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.19927368784541846\n",
-      "2025-11-25 12:27:13,033 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:01<00:00,  2.34it/s]\n",
-      "2025-11-25 12:27:14,746 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|████| 4/4 [00:20<00:00,  5.00s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
-      "2025-11-25 12:27:45,572 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|███████████████████| 22/22 [00:00<00:00, 20369.69it/s]\n",
-      "Computing subpatterns:: 100%|███████████████████| 16/16 [00:05<00:00,  2.80it/s]\n",
-      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    6.7s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    7.0s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    7.5s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    8.3s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   13.0s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   14.6s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   15.1s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   15.4s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   17.9s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   21.8s remaining:   13.1s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   23.8s remaining:   10.8s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   24.4s remaining:    8.1s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   26.7s remaining:    6.2s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   26.8s remaining:    3.8s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   33.9s finished\n",
+      "2026-05-24 22:30:38,588 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.20144225694126183\n",
+      "2026-05-24 22:30:38,588 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.20144192404043892\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.20144200202904422\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.20144200202904422\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.20144200202904422\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.20144225694126183\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.20144192404043892\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.20144200202904422\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.20144200202904422\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.20144192404043892\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.20142729241419652\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.20144200202904422\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.20144192404043892\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.20134915769109862\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.20144225694126183\n",
+      "2026-05-24 22:30:38,589 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.20144225694126183\n",
+      "2026-05-24 22:30:38,595 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
+      "Generating patterns from clusters:: 100%|█████████| 4/4 [00:02<00:00,  1.97it/s]\n",
+      "2026-05-24 22:30:40,623 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|████| 4/4 [00:25<00:00,  6.46s/it]\u001b[0m\u001b[0m\u001b[0m\u001b[0m\n",
+      "2026-05-24 22:31:15,263 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|███████████████████| 19/19 [00:00<00:00, 15736.92it/s]\n",
+      "Computing subpatterns:: 100%|███████████████████| 17/17 [00:53<00:00,  3.12s/it]\n",
+      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
      ]
     }
    ],
@@ -1830,16 +1798,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "id": "2f7726a6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:13:40.222226Z",
-     "iopub.status.busy": "2025-11-21T06:13:40.222071Z",
-     "iopub.status.idle": "2025-11-21T06:13:40.354761Z",
-     "shell.execute_reply": "2025-11-21T06:13:40.354179Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1855,41 +1816,48 @@
       "example/modisco_subcommands/modisco_neurons.seqlets.bed\n",
       "\n",
       "example/modisco_subcommands/modisco_neurons_report:\n",
-      "AHCTF1.H13CORE.0.B.B.png   NKX61.H13CORE.0.PS.A.png\n",
-      "ATF4.H13CORE.2.SM.B.png    NR1D1.H13CORE.0.P.B.png\n",
-      "BACH1.H13CORE.0.P.B.png    NR1I3.H13CORE.1.PSM.A.png\n",
-      "CEBPG.H13CORE.2.P.B.png    NR2F6.H13CORE.3.SM.B.png\n",
-      "CREB1.H13CORE.0.PSM.A.png  PATZ1.H13CORE.0.P.B.png\n",
-      "DDIT3.H13CORE.0.P.B.png    PO5F1.H13CORE.0.P.B.png\n",
-      "ELK4.H13CORE.0.PSM.A.png   PRD13.H13CORE.0.P.B.png\n",
-      "ERR1.H13CORE.0.PSM.A.png   RORG.H13CORE.0.M.C.png\n",
-      "ERR2.H13CORE.0.PSM.A.png   SNAI2.H13CORE.0.PSM.A.png\n",
-      "ERR3.H13CORE.0.PSM.A.png   SOX2.H13CORE.1.P.B.png\n",
-      "ETV4.H13CORE.0.P.B.png\t   SOX3.H13CORE.1.S.C.png\n",
-      "ETV6.H13CORE.0.PS.A.png    SP140L.H13CORE.0.PSGIB.A.png\n",
-      "FEV.H13CORE.0.S.B.png\t   SP2.H13CORE.1.P.B.png\n",
-      "FOSB.H13CORE.1.M.C.png\t   SP3.H13CORE.0.P.B.png\n",
-      "FOS.H13CORE.1.S.C.png\t   SPIB.H13CORE.0.P.B.png\n",
-      "HTF4.H13CORE.0.PSM.A.png   SPIB.H13CORE.1.S.C.png\n",
-      "IRF1.H13CORE.0.P.B.png\t   STAT2.H13CORE.0.P.B.png\n",
-      "IRF4.H13CORE.0.P.B.png\t   TAF1.H13CORE.0.P.B.png\n",
-      "IRF8.H13CORE.0.P.B.png\t   TFE3.H13CORE.0.PSM.A.png\n",
-      "ITF2.H13CORE.1.PSM.A.png   TFEB.H13CORE.0.SM.B.png\n",
-      "JDP2.H13CORE.0.SM.B.png    TFEC.H13CORE.0.M.B.png\n",
-      "JUN.H13CORE.0.P.B.png\t   TFEC.H13CORE.1.SM.B.png\n",
-      "KLF12.H13CORE.0.P.C.png    trimmed_logos\n",
-      "KLF16.H13CORE.1.P.B.png    TYY1.H13CORE.0.PSM.A.png\n",
-      "KLF8.H13CORE.1.P.C.png\t   TYY2.H13CORE.0.PS.A.png\n",
-      "KLF9.H13CORE.1.P.B.png\t   ZBT14.H13CORE.0.P.C.png\n",
-      "KMT2A.H13CORE.0.P.B.png    ZEB1.H13CORE.0.P.B.png\n",
-      "MESP1.H13CORE.0.S.C.png    ZEB2.H13CORE.0.P.B.png\n",
-      "motifs.html\t\t   ZFP28.H13CORE.0.P.B.png\n",
-      "MYC.H13CORE.0.P.B.png\t   ZN37A.H13CORE.0.P.B.png\n",
-      "MYCN.H13CORE.0.PS.A.png    ZN519.H13CORE.0.P.C.png\n",
-      "MYT1.H13CORE.0.SG.A.png    ZN543.H13CORE.0.P.C.png\n",
-      "MYT1L.H13CORE.0.P.C.png    ZN616.H13CORE.0.P.C.png\n",
-      "NANOG.H13CORE.0.P.B.png    ZN778.H13CORE.1.P.B.png\n",
-      "NF2L2.H13CORE.0.P.B.png    ZNF618.H13CORE.0.G.B.png\n"
+      "ATF6A.H13CORE.0.SM.B.png      ONEC2.H13CORE.3.P.B.png\n",
+      "BACH1.H13CORE.0.P.B.png       PKNX1.H13CORE.3.SM.B.png\n",
+      "BATF2.H13CORE.0.PSG.A.png     RFX1.H13CORE.0.PM.A.png\n",
+      "BHA15.H13CORE.0.P.B.png       RFX3.H13CORE.0.PSM.A.png\n",
+      "CREB1.H13CORE.0.PSM.A.png     SATB1.H13CORE.0.P.B.png\n",
+      "DMRT3.H13CORE.0.S.C.png       SKOR1.H13CORE.1.S.C.png\n",
+      "E2F8.H13CORE.1.SM.B.png       SNAI2.H13CORE.0.PSM.A.png\n",
+      "EHF.H13CORE.0.P.B.png\t      SP2.H13CORE.1.P.B.png\n",
+      "EOMES.H13CORE.0.PSM.A.png     SP3.H13CORE.0.P.B.png\n",
+      "ERR3.H13CORE.0.PSM.A.png      SPIB.H13CORE.1.S.C.png\n",
+      "ETV2.H13CORE.0.S.B.png\t      STAT1.H13CORE.1.P.B.png\n",
+      "ETV4.H13CORE.0.P.B.png\t      STAT2.H13CORE.0.P.B.png\n",
+      "ETV5.H13CORE.1.PM.A.png       TAF1.H13CORE.0.P.B.png\n",
+      "ETV6.H13CORE.0.PS.A.png       TFE2.H13CORE.0.PS.A.png\n",
+      "ETV7.H13CORE.0.SM.B.png       trimmed_logos\n",
+      "FBXL19.H13CORE.0.PSGIB.A.png  TYY1.H13CORE.0.PSM.A.png\n",
+      "FEV.H13CORE.0.S.B.png\t      TYY1.H13CORE.1.S.B.png\n",
+      "FOSB.H13CORE.0.P.B.png\t      TYY2.H13CORE.0.PS.A.png\n",
+      "GABPA.H13CORE.0.PSM.A.png     TYY2.H13CORE.1.SM.B.png\n",
+      "HTF4.H13CORE.0.PSM.A.png      ZBTB40.H13CORE.0.PSG.A.png\n",
+      "HXD11.H13CORE.0.PS.A.png      ZEB1.H13CORE.0.P.B.png\n",
+      "IRF3.H13CORE.1.SM.B.png       ZEB2.H13CORE.0.P.B.png\n",
+      "IRF8.H13CORE.0.P.B.png\t      ZFP42.H13CORE.0.PM.A.png\n",
+      "ITF2.H13CORE.1.PSM.A.png      ZFX.H13CORE.0.P.B.png\n",
+      "KLF13.H13CORE.1.P.C.png       ZKSC5.H13CORE.0.P.C.png\n",
+      "KLF16.H13CORE.1.P.B.png       ZN362.H13CORE.0.P.C.png\n",
+      "KLF9.H13CORE.1.P.B.png\t      ZN454.H13CORE.1.SM.B.png\n",
+      "KMT2A.H13CORE.0.P.B.png       ZN483.H13CORE.0.P.C.png\n",
+      "KMT2B.H13CORE.0.P.B.png       ZN510.H13CORE.0.P.C.png\n",
+      "LHX1.H13CORE.1.M.C.png\t      ZN519.H13CORE.0.P.C.png\n",
+      "MAD4.H13CORE.0.P.C.png\t      ZN554.H13CORE.1.P.C.png\n",
+      "MAZ.H13CORE.0.P.B.png\t      ZN584.H13CORE.1.P.C.png\n",
+      "MNT.H13CORE.0.PS.A.png\t      ZN595.H13CORE.0.P.C.png\n",
+      "motifs.html\t\t      ZN616.H13CORE.0.P.C.png\n",
+      "MYC.H13CORE.0.P.B.png\t      ZN716.H13CORE.0.P.C.png\n",
+      "MYSM1.H13CORE.0.P.B.png       ZN778.H13CORE.1.P.B.png\n",
+      "MYT1.H13CORE.0.SG.A.png       ZN784.H13CORE.0.SM.B.png\n",
+      "MYT1L.H13CORE.0.P.C.png       ZNF35.H13CORE.0.P.C.png\n",
+      "NF2L1.H13CORE.0.P.B.png       ZNF575.H13CORE.0.PSG.A.png\n",
+      "NF2L2.H13CORE.0.P.B.png       ZNF618.H13CORE.0.G.B.png\n",
+      "NFAC1.H13CORE.0.P.B.png       ZNF79.H13CORE.0.P.C.png\n",
+      "NR5A2.H13CORE.0.PSM.A.png\n"
      ]
     }
    ],
@@ -1899,27 +1867,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "ba160a3c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:13:40.356169Z",
-     "iopub.status.busy": "2025-11-21T06:13:40.356024Z",
-     "iopub.status.idle": "2025-11-21T06:13:51.327714Z",
-     "shell.execute_reply": "2025-11-21T06:13:51.326802Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.9 (1642.4MB/s)\n",
-      "Processing pos_patterns patterns...: 100%|█████| 19/19 [00:00<00:00, 559.44it/s]\n",
-      "Processing neg_patterns patterns...: 100%|█████| 16/16 [00:00<00:00, 544.08it/s]\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "Processing pos_patterns patterns...: 100%|█████| 19/19 [00:00<00:00, 611.42it/s]\n",
+      "Processing neg_patterns patterns...: 100%|█████| 17/17 [00:00<00:00, 595.77it/s]\n",
       "\u001b[0m"
      ]
     }
@@ -1958,31 +1916,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "id": "0bdfb51a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:13:51.329299Z",
-     "iopub.status.busy": "2025-11-21T06:13:51.329143Z",
-     "iopub.status.idle": "2025-11-21T06:13:51.461661Z",
-     "shell.execute_reply": "2025-11-21T06:13:51.460863Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chr1\t51514319\t51514369\tneg_patterns.pattern_2.seqlet_107.EPS15\t-0.11633501150777192\t-\t51514335\t51514351\t65,105,225\n",
-      "chr1\t51514371\t51514421\tpos_patterns.pattern_3.seqlet_53.EPS15\t0.0075579382651085325\t-\t51514383\t51514417\t65,105,225\n",
-      "chr1\t51514870\t51514920\tpos_patterns.pattern_1.seqlet_61.EPS15\t0.18588461058220673\t-\t51514873\t51514924\t65,105,225\n",
-      "chr1\t51515018\t51515068\tpos_patterns.pattern_1.seqlet_46.EPS15\t0.0422197653533658\t-\t51515027\t51515078\t65,105,225\n",
-      "chr1\t51515049\t51515099\tneg_patterns.pattern_2.seqlet_6.EPS15\t-0.5646011040639678\t+\t51515068\t51515084\t65,105,225\n",
-      "chr1\t51515060\t51515110\tpos_patterns.pattern_9.seqlet_5.EPS15\t-0.49150526542280204\t+\t51515063\t51515108\t65,105,225\n",
-      "chr1\t51515062\t51515112\tpos_patterns.pattern_8.seqlet_40.EPS15\t-0.4699880362472868\t+\t51515058\t51515108\t65,105,225\n",
-      "chr1\t51515163\t51515213\tpos_patterns.pattern_1.seqlet_92.EPS15\t0.13550135794957896\t+\t51515163\t51515214\t65,105,225\n",
-      "chr1\t51517345\t51517395\tpos_patterns.pattern_3.seqlet_72.EPS15\t0.08599610732289875\t+\t51517350\t51517384\t65,105,225\n",
-      "chr1\t51517444\t51517494\tpos_patterns.pattern_18.seqlet_22.EPS15\t0.10237028001802173\t+\t51517448\t51517494\t65,105,225\n"
+      "chr1\t51514320\t51514370\tneg_patterns.pattern_3.seqlet_99.EPS15\t-0.10620923523185866\t-\t51514337\t51514353\t65,105,225\n",
+      "chr1\t51514372\t51514422\tpos_patterns.pattern_9.seqlet_32.EPS15\t0.01187996034286698\t-\t51514379\t51514408\t65,105,225\n",
+      "chr1\t51514855\t51514905\tpos_patterns.pattern_4.seqlet_28.EPS15\t0.20384007981216\t-\t51514855\t51514906\t65,105,225\n",
+      "chr1\t51515016\t51515066\tpos_patterns.pattern_10.seqlet_20.EPS15\t0.10383651407607886\t+\t51515016\t51515067\t65,105,225\n",
+      "chr1\t51515048\t51515098\tneg_patterns.pattern_3.seqlet_4.EPS15\t-0.5593402302795312\t+\t51515066\t51515082\t65,105,225\n",
+      "chr1\t51515063\t51515113\tpos_patterns.pattern_1.seqlet_11.EPS15\t-0.44863525256596404\t-\t51515067\t51515109\t65,105,225\n",
+      "chr1\t51515064\t51515114\tpos_patterns.pattern_4.seqlet_9.EPS15\t-0.4662355634886808\t+\t51515064\t51515115\t65,105,225\n",
+      "chr1\t51515175\t51515225\tpos_patterns.pattern_18.seqlet_23.EPS15\t0.1264613385744724\t+\t51515165\t51515212\t65,105,225\n",
+      "chr1\t51517326\t51517376\tpos_patterns.pattern_9.seqlet_43.EPS15\t0.09152154577770943\t-\t51517339\t51517368\t65,105,225\n",
+      "chr1\t51517434\t51517484\tpos_patterns.pattern_14.seqlet_32.EPS15\t0.04164407310884144\t+\t51517437\t51517472\t65,105,225\n"
      ]
     }
    ],
@@ -2010,61 +1961,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "id": "88ca4c53",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:13:51.463128Z",
-     "iopub.status.busy": "2025-11-21T06:13:51.462984Z",
-     "iopub.status.idle": "2025-11-21T06:14:02.595962Z",
-     "shell.execute_reply": "2025-11-21T06:14:02.595014Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Creating modisco logos for pos_patterns: 100%|██| 19/19 [00:27<00:00,  1.46s/it]\n",
-      "Creating modisco logos for neg_patterns: 100%|██| 16/16 [00:33<00:00,  2.07s/it]\n",
-      "Generating patterns dataframe: 100%|█████████████| 2/2 [00:00<00:00, 236.99it/s]\n",
-      "Reading patterns for pos_patterns: 100%|██████| 19/19 [00:00<00:00, 3145.40it/s]\n",
-      "Reading patterns for neg_patterns: 100%|██████| 16/16 [00:00<00:00, 3140.62it/s]\n",
+      "Creating modisco logos for pos_patterns: 100%|██| 19/19 [00:27<00:00,  1.45s/it]\n",
+      "Creating modisco logos for neg_patterns: 100%|██| 17/17 [00:34<00:00,  2.03s/it]\n",
+      "Generating patterns dataframe: 100%|█████████████| 2/2 [00:00<00:00, 259.41it/s]\n",
+      "Reading patterns for pos_patterns: 100%|██████| 19/19 [00:00<00:00, 3246.76it/s]\n",
+      "Reading patterns for neg_patterns: 100%|██████| 17/17 [00:00<00:00, 3363.36it/s]\n",
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    4.9s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    5.0s\n",
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+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    4.6s\n",
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+      "[Parallel(n_jobs=4)]: Done  14 tasks      | elapsed:  1.5min\n",
+      "[Parallel(n_jobs=4)]: Done  15 tasks      | elapsed:  1.8min\n",
+      "[Parallel(n_jobs=4)]: Done  16 tasks      | elapsed:  2.0min\n",
+      "[Parallel(n_jobs=4)]: Done  17 tasks      | elapsed:  2.0min\n",
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+      "[Parallel(n_jobs=4)]: Done  23 tasks      | elapsed:  2.6min\n",
+      "[Parallel(n_jobs=4)]: Done  24 tasks      | elapsed:  2.6min\n",
+      "[Parallel(n_jobs=4)]: Done  25 tasks      | elapsed:  2.7min\n",
+      "[Parallel(n_jobs=4)]: Done  26 tasks      | elapsed:  2.7min\n",
+      "[Parallel(n_jobs=4)]: Done  27 tasks      | elapsed:  2.7min\n",
       "[Parallel(n_jobs=4)]: Done  28 tasks      | elapsed:  2.8min\n",
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-      "[Parallel(n_jobs=4)]: Done  35 out of  35 | elapsed:  4.6min finished\n",
+      "[Parallel(n_jobs=4)]: Done  29 tasks      | elapsed:  3.0min\n",
+      "[Parallel(n_jobs=4)]: Done  30 out of  36 | elapsed:  3.3min remaining:   39.7s\n",
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+      "[Parallel(n_jobs=4)]: Done  34 out of  36 | elapsed:  4.6min remaining:   16.1s\n",
+      "[Parallel(n_jobs=4)]: Done  36 out of  36 | elapsed:  5.3min finished\n",
       "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
      ]
     }
@@ -2106,10 +2051,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:02.2 (331.4MB/s)\n"
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
      ]
     }
    ],
@@ -2124,28 +2066,19 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "2b1d0936",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:14:02.597653Z",
-     "iopub.status.busy": "2025-11-21T06:14:02.597492Z",
-     "iopub.status.idle": "2025-11-21T06:15:03.292551Z",
-     "shell.execute_reply": "2025-11-21T06:15:03.291762Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:05.2 (599.2MB/s)\n"
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af4ed05ea91c403cbdfa06f2d69da16b",
+       "model_id": "38332a4b42ff49b996762c3160e3633b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2160,16 +2093,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing attributions...: 100%|██████████| 15/15 [00:29<00:00,  2.00s/it]\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.7 (1811.2MB/s)\n"
+      "Computing attributions...: 100%|██████████| 15/15 [00:16<00:00,  1.07s/it]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b9667618f6d64498a76fea152dace751",
+       "model_id": "14280d3ea2b34023b0e07bd1f6d53d31",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2184,27 +2114,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading attributions and sequences...: 100%|██████████| 15/15 [00:09<00:00,  1.60it/s]\n",
-      "2025-11-26 14:21:29,970 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
-      "2025-11-26 14:21:29,974 - modisco-lite - INFO - Extracting seqlets for 15 tasks:\n",
-      "2025-11-26 14:21:29,974 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
-      "2025-11-26 14:21:29,977 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
-      "2025-11-26 14:21:30,027 - modisco-lite - INFO - - Computing isotonic thresholds\n",
-      "2025-11-26 14:21:30,040 - modisco-lite - INFO - - Refining thresholds\n",
-      "2025-11-26 14:21:30,123 - modisco-lite - INFO - - Extracting seqlets\n",
-      "2025-11-26 14:21:30,135 - modisco-lite - INFO - - Extracting 386 positive seqlets\n",
-      "2025-11-26 14:21:30,137 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 386 seqlets\n"
+      "Loading attributions and sequences...: 100%|██████████| 15/15 [00:08<00:00,  1.80it/s]\n",
+      "2026-05-24 22:40:30,321 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
+      "2026-05-24 22:40:30,323 - modisco-lite - INFO - Extracting seqlets for 15 tasks:\n",
+      "2026-05-24 22:40:30,323 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
+      "2026-05-24 22:40:30,327 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
+      "2026-05-24 22:40:30,393 - modisco-lite - INFO - - Computing isotonic thresholds\n",
+      "2026-05-24 22:40:30,407 - modisco-lite - INFO - - Refining thresholds\n",
+      "2026-05-24 22:40:30,504 - modisco-lite - INFO - - Extracting seqlets\n",
+      "2026-05-24 22:40:30,519 - modisco-lite - INFO - - Extracting 395 positive seqlets\n",
+      "2026-05-24 22:40:30,523 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 395 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6185fd937b3c40c689f40a96168f46eb",
+       "model_id": "a5478032c35f44a8abd941e675242ec0",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/386 [00:00<?, ?it/s]"
+       "  0%|          | 0/395 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2214,18 +2144,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:21:41,536 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 386 seqlets and 386 neighbors\n"
+      "2026-05-24 22:40:41,471 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 395 seqlets and 395 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33d96e2876734f11b9c493dafeee6cc5",
+       "model_id": "2e971b26937145be8c3bd827f3a4aaaa",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/386 [00:00<?, ?it/s]"
+       "  0%|          | 0/395 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2234,12 +2164,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b3fa64a521da4df3aaf122b6403b0347",
+       "model_id": "dafb5ef643824ffdbb5eaa49dc838a6c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/386 [00:00<?, ?it/s]"
+       "  0%|          | 0/395 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2249,9 +2179,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:21:43,367 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-26 14:21:43,588 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-26 14:21:44,026 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:40:42,874 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:40:43,091 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:40:43,688 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -2259,69 +2189,69 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    9.5s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    9.6s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    9.8s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    9.9s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:   10.2s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:   10.3s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:   10.5s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:   10.6s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:   10.7s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:   10.8s remaining:    6.5s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:   11.0s remaining:    5.0s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:   11.1s remaining:    3.7s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:   11.3s remaining:    2.6s\n"
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    6.1s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    6.1s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    6.2s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    6.3s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    6.5s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    6.8s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    6.9s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    6.9s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    7.0s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    7.4s remaining:    4.4s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    7.5s remaining:    3.4s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:    7.5s remaining:    2.5s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    7.5s remaining:    1.7s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    7.9s remaining:    1.1s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:21:55,503 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.023795028659783744\n",
-      "2025-11-26 14:21:55,504 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.023809165260847066\n",
-      "2025-11-26 14:21:55,504 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.023809165260847066\n",
-      "2025-11-26 14:21:55,504 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.0237943612948182\n",
-      "2025-11-26 14:21:55,505 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.0237943612948182\n",
-      "2025-11-26 14:21:55,505 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.02377079324490489\n",
-      "2025-11-26 14:21:55,505 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.0237943612948182\n",
-      "2025-11-26 14:21:55,506 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.02381243831842478\n",
-      "2025-11-26 14:21:55,506 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.023809165260847066\n",
-      "2025-11-26 14:21:55,506 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.023795028659783744\n",
-      "2025-11-26 14:21:55,506 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.0237943612948182\n",
-      "2025-11-26 14:21:55,507 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.023777635339003064\n",
-      "2025-11-26 14:21:55,507 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.023777635339003064\n",
-      "2025-11-26 14:21:55,507 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.023777635339003064\n",
-      "2025-11-26 14:21:55,507 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.023777635339003064\n",
-      "2025-11-26 14:21:55,508 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.0237943612948182\n",
-      "2025-11-26 14:21:55,508 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
+      "2026-05-24 22:40:51,829 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.02386268326662939\n",
+      "2026-05-24 22:40:51,829 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.023862110081474437\n",
+      "2026-05-24 22:40:51,829 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.02386268326662939\n",
+      "2026-05-24 22:40:51,830 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.023859167977640844\n",
+      "2026-05-24 22:40:51,830 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.023859837788739707\n",
+      "2026-05-24 22:40:51,830 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.023861050152885862\n",
+      "2026-05-24 22:40:51,830 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.023856484700648745\n",
+      "2026-05-24 22:40:51,831 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.023861050152885862\n",
+      "2026-05-24 22:40:51,831 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.023861050152885862\n",
+      "2026-05-24 22:40:51,831 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.02386268326662939\n",
+      "2026-05-24 22:40:51,831 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.023861050152885862\n",
+      "2026-05-24 22:40:51,832 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.023857660061583314\n",
+      "2026-05-24 22:40:51,832 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.023858374765159167\n",
+      "2026-05-24 22:40:51,832 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.023856484700648745\n",
+      "2026-05-24 22:40:51,832 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.023858374765159167\n",
+      "2026-05-24 22:40:51,833 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.02386268326662939\n",
+      "2026-05-24 22:40:51,833 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:   11.4s remaining:    1.6s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:   11.5s finished\n"
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    8.1s finished\n"
      ]
     },
     {
      "name": "stderr",
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-      "Generating patterns from clusters:: 100%|██████████| 7/7 [00:00<00:00, 18.15it/s]\n",
-      "2025-11-26 14:21:55,896 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 331 seqlets\n"
+      "Generating patterns from clusters:: 100%|██████████| 6/6 [00:00<00:00, 17.06it/s]\n",
+      "2026-05-24 22:40:52,188 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 334 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8863cc251dd84b39acd23fc3676cd850",
+       "model_id": "a53aa392df6c48ebb1aaeba56c3bfaf3",
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-      "2025-11-26 14:21:56,276 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 331 seqlets and 331 neighbors\n"
+      "2026-05-24 22:40:52,511 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 334 seqlets and 334 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78b25d45f543407da96a3fc60708186d",
+       "model_id": "8eed96ba22714371b3c7c226e8f51014",
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-       "model_id": "15c828a4f83e48d4a304c06ae0bc12e5",
+       "model_id": "93e58576e02744dab07ed99710d9f6df",
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:21:58,326 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-26 14:21:58,753 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:40:54,013 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:40:54,593 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -2375,75 +2305,75 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    0.5s\n",
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    0.4s\n",
       "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    0.5s\n",
       "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    0.6s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    0.7s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    1.0s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    0.9s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    0.9s\n",
       "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    1.2s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    1.2s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    1.3s\n",
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+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    1.3s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    1.4s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    1.5s\n",
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      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:22:01,376 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.027972146104404714\n",
-      "2025-11-26 14:22:01,376 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.0279766505739214\n",
-      "2025-11-26 14:22:01,376 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.027859451619283684\n",
-      "2025-11-26 14:22:01,377 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.027978385016837137\n",
-      "2025-11-26 14:22:01,377 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.027978385016837137\n",
-      "2025-11-26 14:22:01,377 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.02795420878142116\n",
-      "2025-11-26 14:22:01,377 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.028083537359749028\n",
-      "2025-11-26 14:22:01,378 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.028087215733838954\n",
-      "2025-11-26 14:22:01,378 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02792621903932944\n",
-      "2025-11-26 14:22:01,378 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.027897467477765375\n",
-      "2025-11-26 14:22:01,379 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.02796967265184125\n",
-      "2025-11-26 14:22:01,379 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.027961431994483324\n",
-      "2025-11-26 14:22:01,379 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.028127934006588935\n",
-      "2025-11-26 14:22:01,379 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.027926915267206393\n",
-      "2025-11-26 14:22:01,380 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.02797661322516535\n",
-      "2025-11-26 14:22:01,380 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.02796967265184125\n",
-      "2025-11-26 14:22:01,380 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n"
+      "2026-05-24 22:40:57,039 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.027521320337249033\n",
+      "2026-05-24 22:40:57,040 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.02754533239246501\n",
+      "2026-05-24 22:40:57,040 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.027300296305956626\n",
+      "2026-05-24 22:40:57,040 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.027578859200674724\n",
+      "2026-05-24 22:40:57,041 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.027394697871717576\n",
+      "2026-05-24 22:40:57,041 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.02754664904855149\n",
+      "2026-05-24 22:40:57,041 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.02758398651436025\n",
+      "2026-05-24 22:40:57,041 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.027476044930561924\n",
+      "2026-05-24 22:40:57,042 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02754533239246501\n",
+      "2026-05-24 22:40:57,042 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.027286110401950947\n",
+      "2026-05-24 22:40:57,042 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.02758398651436025\n",
+      "2026-05-24 22:40:57,042 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.027588792989889654\n",
+      "2026-05-24 22:40:57,043 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.02729878720105619\n",
+      "2026-05-24 22:40:57,043 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.027422305100453556\n",
+      "2026-05-24 22:40:57,043 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.027300296305956626\n",
+      "2026-05-24 22:40:57,043 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.0275701597639355\n",
+      "2026-05-24 22:40:57,044 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    2.6s finished\n"
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    2.4s finished\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating patterns from clusters:: 100%|██████████| 6/6 [00:00<00:00, 14.19it/s]\n",
-      "2025-11-26 14:22:01,806 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|██████████| 5/5 [00:07<00:00,  1.57s/it]\n",
-      "2025-11-26 14:22:11,338 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|██████████| 13/13 [00:00<00:00, 14024.16it/s]\n",
-      "Computing subpatterns:: 100%|██████████| 9/9 [00:00<00:00, 43.79it/s]\n",
-      "2025-11-26 14:22:11,548 - modisco-lite - INFO - - Extracting 339 negative seqlets\n",
-      "2025-11-26 14:22:11,550 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 339 seqlets\n"
+      "Generating patterns from clusters:: 100%|██████████| 5/5 [00:00<00:00, 13.53it/s]\n",
+      "2026-05-24 22:40:57,416 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|██████████| 5/5 [00:20<00:00,  4.08s/it]\n",
+      "2026-05-24 22:41:19,189 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|██████████| 16/16 [00:00<00:00, 22265.71it/s]\n",
+      "Computing subpatterns:: 100%|██████████| 8/8 [00:33<00:00,  4.18s/it]\n",
+      "2026-05-24 22:41:52,635 - modisco-lite - INFO - - Extracting 338 negative seqlets\n",
+      "2026-05-24 22:41:52,638 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 338 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a8bfc9c161a54ba7b54d212a7ad6e749",
+       "model_id": "2f081f12cdbc450b9f391682a6ad0335",
        "version_major": 2,
        "version_minor": 0
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:22:11,965 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 339 seqlets and 339 neighbors\n"
+      "2026-05-24 22:41:53,038 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 338 seqlets and 338 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5a95aa65e1294e79bf959ba53e31df53",
+       "model_id": "dd1efde8ec7b4bbbb61ce94b968e51a6",
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     {
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-       "model_id": "d4c6cfba72b940ad859bd3540458d63e",
+       "model_id": "a3ede5aac07f4fa9bbc33e5d8fb6fb73",
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:22:13,417 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-26 14:22:13,593 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-26 14:22:13,892 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:41:54,070 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:41:54,242 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:41:54,653 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -2498,69 +2428,69 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    5.1s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    5.2s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    5.2s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    5.3s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    5.7s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    5.7s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    5.7s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    5.8s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    5.9s\n",
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+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    7.6s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    7.7s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    7.8s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    7.9s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    8.0s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    8.1s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    8.2s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    8.3s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    8.4s\n",
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      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:22:20,432 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.024608354166690688\n",
-      "2025-11-26 14:22:20,432 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.024555206014402104\n",
-      "2025-11-26 14:22:20,433 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.024608619101050493\n",
-      "2025-11-26 14:22:20,433 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02430075643539113\n",
-      "2025-11-26 14:22:20,433 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.024604898186772882\n",
-      "2025-11-26 14:22:20,433 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.024576295518500393\n",
-      "2025-11-26 14:22:20,434 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.024609772726449097\n",
-      "2025-11-26 14:22:20,434 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.024492250787893233\n",
-      "2025-11-26 14:22:20,434 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.024479953670128285\n",
-      "2025-11-26 14:22:20,434 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.024616179702116665\n",
-      "2025-11-26 14:22:20,435 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.024535146927203558\n",
-      "2025-11-26 14:22:20,435 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.024576295518500393\n",
-      "2025-11-26 14:22:20,436 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.024621444156624782\n",
-      "2025-11-26 14:22:20,436 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.024609772726449097\n",
-      "2025-11-26 14:22:20,436 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.024316307402529776\n",
-      "2025-11-26 14:22:20,436 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.024261986849680905\n",
-      "2025-11-26 14:22:20,437 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
+      "2026-05-24 22:42:03,620 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.02469193249495884\n",
+      "2026-05-24 22:42:03,620 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.024690890848125095\n",
+      "2026-05-24 22:42:03,621 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.024664980051820655\n",
+      "2026-05-24 22:42:03,621 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02467120799921634\n",
+      "2026-05-24 22:42:03,621 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.024666090731021575\n",
+      "2026-05-24 22:42:03,621 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.024666090731021575\n",
+      "2026-05-24 22:42:03,622 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.024239914615136044\n",
+      "2026-05-24 22:42:03,622 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.024683564994529988\n",
+      "2026-05-24 22:42:03,622 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02469193249495884\n",
+      "2026-05-24 22:42:03,622 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.024700394623705227\n",
+      "2026-05-24 22:42:03,623 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.024690890848125095\n",
+      "2026-05-24 22:42:03,624 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.02412600257073717\n",
+      "2026-05-24 22:42:03,624 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.024323355226962697\n",
+      "2026-05-24 22:42:03,624 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.024678966153257986\n",
+      "2026-05-24 22:42:03,624 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.02422204614765378\n",
+      "2026-05-24 22:42:03,625 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.02469660469365375\n",
+      "2026-05-24 22:42:03,625 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    6.5s finished\n"
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    8.8s remaining:    2.0s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    8.8s remaining:    1.3s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    9.0s finished\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating patterns from clusters:: 100%|██████████| 5/5 [00:00<00:00, 12.94it/s]\n",
-      "2025-11-26 14:22:20,825 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 275 seqlets\n"
+      "Generating patterns from clusters:: 100%|██████████| 6/6 [00:00<00:00, 22.03it/s]\n",
+      "2026-05-24 22:42:03,899 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 276 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13debe9c28784f6dbddb547b448a0aaf",
+       "model_id": "286362d593ab40c19b59cca06a0e448e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/275 [00:00<?, ?it/s]"
+       "  0%|          | 0/276 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2570,18 +2500,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:22:21,114 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 275 seqlets and 275 neighbors\n"
+      "2026-05-24 22:42:04,158 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 276 seqlets and 276 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ded5bf094b304fb5ae9136e8509bc54b",
+       "model_id": "f7f754bc0db448abaccef24e9ee20603",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/276 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2590,12 +2520,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "12c461d43d074d128321495620a4c6bf",
+       "model_id": "696b3506551445f49ee76cba5fcc2fb1",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/275 [00:00<?, ?it/s]"
+       "  0%|          | 0/276 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2605,8 +2535,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:22:22,530 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-26 14:22:22,818 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:42:05,191 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:42:05,587 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -2617,40 +2547,40 @@
       "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    0.3s\n",
       "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    0.4s\n",
       "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    0.5s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    0.7s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    0.5s\n",
       "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    0.7s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    0.8s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    1.0s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    1.0s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    1.1s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    1.2s remaining:    0.7s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    1.3s remaining:    0.6s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:    1.5s remaining:    0.5s\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    1.5s remaining:    0.3s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    1.6s remaining:    0.2s\n"
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    0.7s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    0.8s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    0.9s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    1.0s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    1.1s remaining:    0.7s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    1.2s remaining:    0.5s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:    1.3s remaining:    0.4s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    1.4s remaining:    0.3s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    1.5s remaining:    0.2s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:22:24,635 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.027310578905424973\n",
-      "2025-11-26 14:22:24,635 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.027274382988492298\n",
-      "2025-11-26 14:22:24,636 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.0272036592966308\n",
-      "2025-11-26 14:22:24,636 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.027208516969513756\n",
-      "2025-11-26 14:22:24,636 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.02728557611689632\n",
-      "2025-11-26 14:22:24,636 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.027277733838769667\n",
-      "2025-11-26 14:22:24,637 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.02728017408629799\n",
-      "2025-11-26 14:22:24,637 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.027266077516972378\n",
-      "2025-11-26 14:22:24,637 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.027218833035909926\n",
-      "2025-11-26 14:22:24,637 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.027228507749343173\n",
-      "2025-11-26 14:22:24,638 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.027287662177375882\n",
-      "2025-11-26 14:22:24,638 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.027228507749343173\n",
-      "2025-11-26 14:22:24,638 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.02727254658919197\n",
-      "2025-11-26 14:22:24,638 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.02732835815403634\n",
-      "2025-11-26 14:22:24,639 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.027266525132900985\n",
-      "2025-11-26 14:22:24,639 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.027276941565096365\n",
-      "2025-11-26 14:22:24,639 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n"
+      "2026-05-24 22:42:07,411 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.02793115935049446\n",
+      "2026-05-24 22:42:07,411 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.02795007070348518\n",
+      "2026-05-24 22:42:07,412 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.02791333362747872\n",
+      "2026-05-24 22:42:07,412 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02809900456669836\n",
+      "2026-05-24 22:42:07,412 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.02784402800146385\n",
+      "2026-05-24 22:42:07,412 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.028089481353861974\n",
+      "2026-05-24 22:42:07,413 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.02796279961338436\n",
+      "2026-05-24 22:42:07,413 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.028095639862386383\n",
+      "2026-05-24 22:42:07,413 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.027980793240086437\n",
+      "2026-05-24 22:42:07,413 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.028087162238802567\n",
+      "2026-05-24 22:42:07,414 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.027936180542778112\n",
+      "2026-05-24 22:42:07,414 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.02808546919983113\n",
+      "2026-05-24 22:42:07,414 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.027930952110593103\n",
+      "2026-05-24 22:42:07,414 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.027950515037263576\n",
+      "2026-05-24 22:42:07,415 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.0279333015890114\n",
+      "2026-05-24 22:42:07,415 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.02809900456669836\n",
+      "2026-05-24 22:42:07,415 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n"
      ]
     },
     {
@@ -2664,17 +2594,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating patterns from clusters:: 100%|██████████| 7/7 [00:00<00:00, 14.31it/s]\n",
-      "2025-11-26 14:22:25,131 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|██████████| 6/6 [00:05<00:00,  1.10it/s]\n",
-      "2025-11-26 14:22:31,892 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|██████████| 12/12 [00:00<00:00, 23618.79it/s]\n",
-      "Computing subpatterns:: 100%|██████████| 6/6 [00:00<00:00, 40.82it/s]\n",
-      "Creating modisco logos for pos_patterns: 100%|██████████| 9/9 [00:18<00:00,  2.01s/it]\n",
-      "Creating modisco logos for neg_patterns: 100%|██████████| 6/6 [00:14<00:00,  2.39s/it]\n",
-      "Generating patterns dataframe: 100%|██████████| 2/2 [00:00<00:00, 483.27it/s]\n",
-      "Reading patterns for pos_patterns: 100%|██████████| 9/9 [00:00<00:00, 2959.29it/s]\n",
-      "Reading patterns for neg_patterns: 100%|██████████| 6/6 [00:00<00:00, 2759.41it/s]\n"
+      "Generating patterns from clusters:: 100%|██████████| 7/7 [00:00<00:00, 25.45it/s]\n",
+      "2026-05-24 22:42:07,693 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|██████████| 7/7 [00:10<00:00,  1.46s/it]\n",
+      "2026-05-24 22:42:18,839 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|██████████| 13/13 [00:00<00:00, 20907.19it/s]\n",
+      "Computing subpatterns:: 100%|██████████| 7/7 [00:29<00:00,  4.14s/it]\n",
+      "Creating modisco logos for pos_patterns: 100%|██████████| 8/8 [00:15<00:00,  1.90s/it]\n",
+      "Creating modisco logos for neg_patterns: 100%|██████████| 7/7 [00:18<00:00,  2.60s/it]\n",
+      "Generating patterns dataframe: 100%|██████████| 2/2 [00:00<00:00, 501.83it/s]\n",
+      "Reading patterns for pos_patterns: 100%|██████████| 8/8 [00:00<00:00, 2919.05it/s]\n",
+      "Reading patterns for neg_patterns: 100%|██████████| 7/7 [00:00<00:00, 3134.09it/s]\n"
      ]
     },
     {
@@ -2682,31 +2612,7 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   15.3s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:  1.6min\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:  1.9min\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:  2.1min\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:  2.5min\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:  2.9min\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:  2.9min\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:  3.3min\n",
-      "[Parallel(n_jobs=4)]: Done   9 out of  15 | elapsed:  3.7min remaining:  2.4min\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  15 | elapsed:  3.8min remaining:  1.9min\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  15 | elapsed:  3.9min remaining:  1.4min\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  15 | elapsed:  4.1min remaining:  1.0min\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  15 | elapsed:  5.0min remaining:   46.4s\n",
-      "[Parallel(n_jobs=4)]: Done  15 out of  15 | elapsed:  5.9min finished\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.8 (1780.7MB/s)\n",
-      "Processing pos_patterns patterns...: 100%|██████████| 9/9 [00:00<00:00, 431.70it/s]\n",
-      "Processing neg_patterns patterns...: 100%|██████████| 6/6 [00:00<00:00, 531.27it/s]\n"
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    7.5s\n"
      ]
     }
    ],
@@ -2733,16 +2639,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "a32613a8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:15:03.294309Z",
-     "iopub.status.busy": "2025-11-21T06:15:03.294125Z",
-     "iopub.status.idle": "2025-11-21T06:15:03.296680Z",
-     "shell.execute_reply": "2025-11-21T06:15:03.296186Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from decima.interpret.modisco import (\n",
@@ -2763,30 +2662,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "88fd6bd2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:15:03.297811Z",
-     "iopub.status.busy": "2025-11-21T06:15:03.297686Z",
-     "iopub.status.idle": "2025-11-21T06:15:33.908321Z",
-     "shell.execute_reply": "2025-11-21T06:15:33.907579Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.8 (1719.7MB/s)\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d4f2df651f3d414aabce56d80f692dc8",
+       "model_id": "9b140e5ebbe14c699e62b8c7b1bb29bf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2801,7 +2684,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing attributions...: 100%|██████████| 15/15 [00:24<00:00,  1.64s/it]\n"
+      "Computing attributions...: 100%|██████████| 15/15 [00:16<00:00,  1.09s/it]\n"
      ]
     },
     {
@@ -2810,7 +2693,7 @@
        "PosixPath('example/modisco_python_subcommands/modisco_fibroblast_0.attributions.h5')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2831,38 +2714,19 @@
    "id": "cecfe355",
    "metadata": {},
    "source": [
-    "Lets run it for the second replicate:"
+    "Let's run it for the second replicate:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "8049bc6d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:15:33.910125Z",
-     "iopub.status.busy": "2025-11-21T06:15:33.909953Z",
-     "iopub.status.idle": "2025-11-21T06:16:03.244973Z",
-     "shell.execute_reply": "2025-11-21T06:16:03.244221Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep1:latest', 720.03MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.5 (485.5MB/s)\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.8 (1735.5MB/s)\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f1fdcfd0e1c4861a5e40b660bb083ab",
+       "model_id": "1224afe3888c4c8290dd4cea13d7cca7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2877,7 +2741,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing attributions...: 100%|██████████| 15/15 [00:24<00:00,  1.64s/it]\n"
+      "Computing attributions...: 100%|██████████| 15/15 [00:15<00:00,  1.03s/it]\n"
      ]
     },
     {
@@ -2886,7 +2750,7 @@
        "PosixPath('example/modisco_python_subcommands/modisco_fibroblast_1.attributions.h5')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2916,29 +2780,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "79f74f82",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:16:03.247921Z",
-     "iopub.status.busy": "2025-11-21T06:16:03.247305Z",
-     "iopub.status.idle": "2025-11-21T06:16:03.662978Z",
-     "shell.execute_reply": "2025-11-21T06:16:03.662261Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Genes:  ['ANKRD1', 'CASQ2', 'TBX20', 'MYOZ2', 'HSPB3', 'PLN', 'NPPA', 'SMCO1', 'POPDC2', 'TNNI3', 'MYL7', 'ASB11', 'RBM24', 'NPPB', 'MIR133A1HG']\n"
+      "Genes:  ['ANKRD1' 'CASQ2' 'TBX20' 'MYOZ2' 'HSPB3' 'PLN' 'NPPA' 'SMCO1' 'POPDC2'\n",
+      " 'TNNI3' 'MYL7' 'ASB11' 'RBM24' 'NPPB' 'MIR133A1HG']\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading attributions and sequences...: 100%|██████████| 5/5 [00:00<00:00, 4469.63it/s]\n"
+      "Loading attributions and sequences...: 100%|██████████| 5/5 [00:00<00:00, 1316.98it/s]\n"
      ]
     },
     {
@@ -2969,50 +2827,50 @@
       "  [0. 0. 1. ... 0. 0. 0.]\n",
       "  [0. 1. 0. ... 0. 0. 1.]\n",
       "  [1. 0. 0. ... 0. 1. 0.]]]\n",
-      "Attrs:  [[[ 4.11669953e-06  3.51102881e-06  7.94160951e-06 ... -6.51184346e-04\n",
-      "    4.03336322e-04  3.11743590e-04]\n",
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-      "   -5.04222646e-04 -1.85407145e-04]\n",
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-      "   -4.59062685e-05  1.51909100e-04]\n",
-      "  [ 1.08772398e-05  8.22494394e-06  4.27554914e-06 ...  1.91271689e-04\n",
-      "    4.14176225e-04  2.11419644e-04]]\n",
+      "Attrs:  [[[ 3.94093922e-06  3.36339599e-06  7.82327515e-06 ... -6.65014253e-04\n",
+      "    4.06923646e-04  3.16976482e-04]\n",
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+      "   -3.83116057e-05  1.54723921e-04]\n",
+      "  [ 1.06359795e-05  8.06799380e-06  4.44841498e-06 ...  2.05638960e-04\n",
+      "    4.02964262e-04  2.12837056e-04]]\n",
       "\n",
-      " [[-7.12776909e-07 -8.51829304e-06 -1.00335756e-05 ...  3.64433295e-04\n",
-      "   -1.68165593e-04 -2.15544297e-05]\n",
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-      "   -4.67266022e-04  3.97110462e-05]\n",
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-      "    1.14153904e-04  1.41842081e-04]\n",
-      "  [-7.94581001e-06 -1.03179825e-05 -1.15250946e-05 ...  1.11890486e-04\n",
-      "    1.03366935e-04 -3.83914953e-05]]\n",
+      " [[-7.01846830e-07 -8.62049455e-06 -1.02042454e-05 ...  3.63305312e-04\n",
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+      "   -4.65150566e-04  3.65288797e-05]\n",
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+      "    1.14798970e-04  1.39241249e-04]\n",
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+      "    1.08380802e-04 -3.58827410e-05]]\n",
       "\n",
-      " [[ 6.22931702e-06 -2.40001968e-06  3.99901114e-06 ...  1.27383357e-04\n",
-      "   -3.98613782e-05  3.65746330e-05]\n",
-      "  [-1.04496144e-06 -5.69107658e-06  1.58684402e-05 ...  1.81218707e-04\n",
-      "   -8.53879483e-05  2.34974323e-06]\n",
-      "  [-1.34523748e-06 -4.12425379e-07 -1.35589685e-05 ... -2.36304879e-04\n",
-      "    5.85314444e-05  5.42831878e-05]\n",
-      "  [-3.28072201e-06 -8.96757751e-06  2.29386666e-06 ...  4.85571622e-05\n",
-      "    1.34285559e-04 -8.72131452e-05]]\n",
+      " [[ 6.07414859e-06 -2.33285724e-06  3.92410789e-06 ...  1.28538282e-04\n",
+      "   -4.08722233e-05  3.70230870e-05]\n",
+      "  [-8.67197286e-07 -5.34188018e-06  1.57190543e-05 ...  1.83306803e-04\n",
+      "   -8.48834859e-05  2.13613257e-06]\n",
+      "  [-1.22631263e-06 -5.43860324e-07 -1.32591176e-05 ... -2.37786257e-04\n",
+      "    5.95750664e-05  5.47572453e-05]\n",
+      "  [-3.20629533e-06 -8.83861594e-06  2.11010693e-06 ...  4.94425581e-05\n",
+      "    1.35580078e-04 -8.76973809e-05]]\n",
       "\n",
-      " [[-1.37954644e-05 -3.90460445e-06  6.87959073e-07 ...  3.65188289e-05\n",
-      "    1.63221080e-05 -4.28942469e-05]\n",
-      "  [ 2.81759449e-05  2.28448725e-05  9.60779835e-06 ... -5.25707023e-05\n",
-      "    5.20869717e-05 -1.49518037e-05]\n",
-      "  [-2.53114367e-05 -5.28540295e-06 -3.19789835e-05 ...  2.62528688e-05\n",
-      "   -4.93487964e-05 -1.95848042e-06]\n",
-      "  [ 1.46614780e-05 -1.01800415e-05  5.92166180e-06 ...  6.63946475e-05\n",
-      "   -2.03328295e-05  1.19883866e-05]]\n",
+      " [[-1.36747212e-05 -5.09496022e-06  5.77730134e-07 ...  3.50785904e-05\n",
+      "    1.80259588e-05 -4.23415074e-05]\n",
+      "  [ 2.71184927e-05  2.33650489e-05  9.27680958e-06 ... -5.06292959e-05\n",
+      "    5.12533015e-05 -1.42350123e-05]\n",
+      "  [-2.49543864e-05 -6.57394412e-06 -3.15573116e-05 ...  2.60394555e-05\n",
+      "   -5.02181274e-05 -3.02280159e-06]\n",
+      "  [ 1.53254409e-05 -9.86687155e-06  6.17147089e-06 ...  6.47798042e-05\n",
+      "   -1.97583240e-05  1.19098700e-05]]\n",
       "\n",
-      " [[-5.80844272e-06 -5.40845258e-06  6.25991788e-06 ... -2.11186986e-05\n",
-      "    2.25048857e-05 -9.42733004e-06]\n",
-      "  [ 5.52965048e-06  2.58429304e-06  6.51601476e-06 ...  9.90043429e-06\n",
-      "    1.70203975e-06  4.89768377e-06]\n",
-      "  [-1.68889705e-05  1.75423449e-05  3.39855230e-05 ...  9.76046533e-06\n",
-      "    3.77160331e-05  6.74790181e-06]\n",
-      "  [ 8.32652177e-07 -1.08824670e-05 -8.32292460e-06 ...  2.34976978e-05\n",
-      "   -4.02709395e-05  2.35174830e-05]]]\n"
+      " [[-5.85110973e-06 -5.21604397e-06  6.19394177e-06 ... -2.10927599e-05\n",
+      "    2.30072133e-05 -9.33994170e-06]\n",
+      "  [ 5.53593122e-06  2.45451611e-06  6.90793740e-06 ...  1.00862925e-05\n",
+      "    1.87246769e-06  4.98598092e-06]\n",
+      "  [-1.69217678e-05  1.76240395e-05  3.42491009e-05 ...  1.02226414e-05\n",
+      "    3.79763042e-05  6.58357408e-06]\n",
+      "  [ 7.96392897e-07 -1.08680267e-05 -8.37411494e-06 ...  2.39099550e-05\n",
+      "   -4.05807482e-05  2.38762250e-05]]]\n"
      ]
     }
    ],
@@ -3045,30 +2903,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "220cc74d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:16:03.665084Z",
-     "iopub.status.busy": "2025-11-21T06:16:03.664863Z",
-     "iopub.status.idle": "2025-11-21T06:16:09.300425Z",
-     "shell.execute_reply": "2025-11-21T06:16:09.299951Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.7 (1808.6MB/s)\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d95dd65d4fe2475b939f7aaddf351b3e",
+       "model_id": "3b837865b6634fd2a46a4bc32eb2c669",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3083,36 +2925,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading attributions and sequences...: 100%|██████████| 15/15 [00:09<00:00,  1.66it/s]\n",
-      "2025-11-26 14:41:30,775 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
-      "2025-11-26 14:41:30,775 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
-      "2025-11-26 14:41:30,779 - modisco-lite - INFO - Extracting seqlets for 15 tasks:\n",
-      "2025-11-26 14:41:30,779 - modisco-lite - INFO - Extracting seqlets for 15 tasks:\n",
-      "2025-11-26 14:41:30,779 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
-      "2025-11-26 14:41:30,779 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
-      "2025-11-26 14:41:30,783 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
-      "2025-11-26 14:41:30,783 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
-      "2025-11-26 14:41:30,835 - modisco-lite - INFO - - Computing isotonic thresholds\n",
-      "2025-11-26 14:41:30,835 - modisco-lite - INFO - - Computing isotonic thresholds\n",
-      "2025-11-26 14:41:30,853 - modisco-lite - INFO - - Refining thresholds\n",
-      "2025-11-26 14:41:30,853 - modisco-lite - INFO - - Refining thresholds\n",
-      "2025-11-26 14:41:30,944 - modisco-lite - INFO - - Extracting seqlets\n",
-      "2025-11-26 14:41:30,944 - modisco-lite - INFO - - Extracting seqlets\n",
-      "2025-11-26 14:41:30,957 - modisco-lite - INFO - - Extracting 357 positive seqlets\n",
-      "2025-11-26 14:41:30,957 - modisco-lite - INFO - - Extracting 357 positive seqlets\n",
-      "2025-11-26 14:41:30,960 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 357 seqlets\n",
-      "2025-11-26 14:41:30,960 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 357 seqlets\n"
+      "Loading attributions and sequences...: 100%|██████████| 15/15 [00:10<00:00,  1.46it/s]\n",
+      "2026-05-24 22:49:58,978 - modisco-lite - INFO - Running TFMoDISco version 2.4.0\n",
+      "2026-05-24 22:49:58,980 - modisco-lite - INFO - Extracting seqlets for 15 tasks:\n",
+      "2026-05-24 22:49:58,980 - modisco-lite - INFO - - Smoothing and splitting tracks\n",
+      "2026-05-24 22:49:58,985 - modisco-lite - INFO - - Computing null values with Laplacian null model\n",
+      "2026-05-24 22:49:59,047 - modisco-lite - INFO - - Computing isotonic thresholds\n",
+      "2026-05-24 22:49:59,059 - modisco-lite - INFO - - Refining thresholds\n",
+      "2026-05-24 22:49:59,155 - modisco-lite - INFO - - Extracting seqlets\n",
+      "2026-05-24 22:49:59,170 - modisco-lite - INFO - - Extracting 439 positive seqlets\n",
+      "2026-05-24 22:49:59,174 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 439 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bbda3f9363bb4579b92acf4f217a95f0",
+       "model_id": "b084b54abcf74c6cb212f3419e1ef2ef",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/357 [00:00<?, ?it/s]"
+       "  0%|          | 0/439 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -3122,19 +2955,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:31,435 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 357 seqlets and 357 neighbors\n",
-      "2025-11-26 14:41:31,435 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 357 seqlets and 357 neighbors\n"
+      "2026-05-24 22:50:10,154 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 439 seqlets and 439 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6187915f828e4a28b44cb4fdd50e60a5",
+       "model_id": "d0f351c1403d48ceacd0063d2f7ead7d",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/357 [00:00<?, ?it/s]"
+       "  0%|          | 0/439 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -3143,12 +2975,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0abfffc0d8824ba8ad661d9f03c29f97",
+       "model_id": "a7bcb077a9e146909b1655366911e33e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/357 [00:00<?, ?it/s]"
+       "  0%|          | 0/439 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -3158,12 +2990,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:32,984 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-26 14:41:32,984 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-26 14:41:33,194 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-26 14:41:33,194 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-26 14:41:33,623 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
-      "2025-11-26 14:41:33,623 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:50:11,840 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:50:12,100 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:50:12,896 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -3171,87 +3000,69 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    7.0s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    7.0s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    7.1s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    7.2s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    7.6s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    7.8s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    7.8s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    7.9s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    8.2s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    8.3s remaining:    5.0s\n",
-      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    8.3s remaining:    3.8s\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:    8.3s remaining:    2.8s\n"
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    6.1s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    6.3s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    6.5s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    6.6s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    6.8s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    7.5s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    7.5s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    7.5s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    7.6s\n",
+      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    8.1s remaining:    4.9s\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  16 | elapsed:    8.1s remaining:    3.7s\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  16 | elapsed:    8.3s remaining:    2.8s\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    8.5s remaining:    2.0s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    8.9s remaining:    1.3s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:42,502 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,502 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,503 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.023057628701554762\n",
-      "2025-11-26 14:41:42,503 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.023057628701554762\n",
-      "2025-11-26 14:41:42,504 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.023063756534799088\n",
-      "2025-11-26 14:41:42,504 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.023063756534799088\n",
-      "2025-11-26 14:41:42,504 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02307444688993521\n",
-      "2025-11-26 14:41:42,504 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02307444688993521\n",
-      "2025-11-26 14:41:42,505 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.023057150250069196\n",
-      "2025-11-26 14:41:42,505 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.023057150250069196\n",
-      "2025-11-26 14:41:42,505 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,505 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,506 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,506 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,507 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.023093229770165938\n",
-      "2025-11-26 14:41:42,507 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.023093229770165938\n",
-      "2025-11-26 14:41:42,507 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,507 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02309261121756695\n",
-      "2025-11-26 14:41:42,508 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.02309725538927325\n",
-      "2025-11-26 14:41:42,508 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.02309725538927325\n",
-      "2025-11-26 14:41:42,508 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.023042117055525172\n",
-      "2025-11-26 14:41:42,508 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.023042117055525172\n",
-      "2025-11-26 14:41:42,509 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.0228941134391628\n",
-      "2025-11-26 14:41:42,509 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.0228941134391628\n",
-      "2025-11-26 14:41:42,509 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.02309725538927325\n",
-      "2025-11-26 14:41:42,509 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.02309725538927325\n",
-      "2025-11-26 14:41:42,510 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.023054464600618312\n",
-      "2025-11-26 14:41:42,510 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.023054464600618312\n",
-      "2025-11-26 14:41:42,510 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.023094068851621772\n",
-      "2025-11-26 14:41:42,510 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.023094068851621772\n",
-      "2025-11-26 14:41:42,511 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.023058761826335157\n",
-      "2025-11-26 14:41:42,511 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.023058761826335157\n",
-      "2025-11-26 14:41:42,511 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
-      "2025-11-26 14:41:42,511 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
+      "2026-05-24 22:50:22,162 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.02133166977580497\n",
+      "2026-05-24 22:50:22,162 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.021331780342428816\n",
+      "2026-05-24 22:50:22,162 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.021324063933981067\n",
+      "2026-05-24 22:50:22,163 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.021323622125744634\n",
+      "2026-05-24 22:50:22,163 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.021326554293395555\n",
+      "2026-05-24 22:50:22,163 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.021332253345693063\n",
+      "2026-05-24 22:50:22,163 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.021331829897138742\n",
+      "2026-05-24 22:50:22,164 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.021151339005336136\n",
+      "2026-05-24 22:50:22,164 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.021324192234607648\n",
+      "2026-05-24 22:50:22,164 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.021329633090249274\n",
+      "2026-05-24 22:50:22,164 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.02133166977580497\n",
+      "2026-05-24 22:50:22,165 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.021331829897138742\n",
+      "2026-05-24 22:50:22,165 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.02133166977580497\n",
+      "2026-05-24 22:50:22,165 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.021319733032962307\n",
+      "2026-05-24 22:50:22,165 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.021317128147134996\n",
+      "2026-05-24 22:50:22,166 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.02132502593613604\n",
+      "2026-05-24 22:50:22,166 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    8.7s remaining:    2.0s\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    8.7s remaining:    1.2s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    8.9s finished\n"
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    9.3s finished\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating patterns from clusters:: 100%|██████████| 5/5 [00:00<00:00, 11.62it/s]\n",
-      "2025-11-26 14:41:42,945 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 323 seqlets\n",
-      "2025-11-26 14:41:42,945 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 323 seqlets\n"
+      "Generating patterns from clusters:: 100%|██████████| 5/5 [00:00<00:00, 11.52it/s]\n",
+      "2026-05-24 22:50:22,603 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 390 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dbf862d1a75f4c298328b29c3fb48e08",
+       "model_id": "b22fca4775da476d9923fc13e4faa98e",
        "version_major": 2,
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:43,283 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 323 seqlets and 323 neighbors\n",
-      "2025-11-26 14:41:43,283 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 323 seqlets and 323 neighbors\n"
+      "2026-05-24 22:50:22,995 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 390 seqlets and 390 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fbf691ece423432aa5497f1e07d37a42",
+       "model_id": "d3b57205f69c4e15a33b1ac0435c3505",
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-       "model_id": "558fae2f488240dc8dc9d6face2d62bb",
+       "model_id": "401a028ede5a4688bf2daeb10c3b04e0",
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:45,199 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-26 14:41:45,199 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-26 14:41:45,616 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
-      "2025-11-26 14:41:45,616 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:50:25,054 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:50:25,807 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -3308,96 +3116,75 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    0.5s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    0.6s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    0.7s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    0.9s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    1.0s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    1.3s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    1.3s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    1.4s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    1.5s\n",
-      "[Parallel(n_jobs=4)]: Done  10 out of  16 | elapsed:    1.8s remaining:    1.1s\n",
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+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    0.6s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    0.8s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    0.8s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    0.8s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    1.4s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    1.6s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    1.6s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    1.6s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    2.2s\n",
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+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    3.0s remaining:    0.4s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:48,209 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.026879757827424727\n",
-      "2025-11-26 14:41:48,209 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.026879757827424727\n",
-      "2025-11-26 14:41:48,210 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.026907131992464417\n",
-      "2025-11-26 14:41:48,210 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.026907131992464417\n",
-      "2025-11-26 14:41:48,211 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.026654374342843252\n",
-      "2025-11-26 14:41:48,211 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.026654374342843252\n",
-      "2025-11-26 14:41:48,212 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.026853731327052162\n",
-      "2025-11-26 14:41:48,212 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.026853731327052162\n",
-      "2025-11-26 14:41:48,212 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.026832301306894967\n",
-      "2025-11-26 14:41:48,212 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.026832301306894967\n",
-      "2025-11-26 14:41:48,213 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.026821856499785617\n",
-      "2025-11-26 14:41:48,213 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.026821856499785617\n",
-      "2025-11-26 14:41:48,213 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.026927867658082184\n",
-      "2025-11-26 14:41:48,213 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.026927867658082184\n",
-      "2025-11-26 14:41:48,214 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.026895004221572465\n",
-      "2025-11-26 14:41:48,214 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.026895004221572465\n",
-      "2025-11-26 14:41:48,214 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.026935597558394527\n",
-      "2025-11-26 14:41:48,214 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.026935597558394527\n",
-      "2025-11-26 14:41:48,215 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.026927867658082184\n",
-      "2025-11-26 14:41:48,215 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.026927867658082184\n",
-      "2025-11-26 14:41:48,216 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.026891895663637772\n",
-      "2025-11-26 14:41:48,216 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.026891895663637772\n",
-      "2025-11-26 14:41:48,217 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.026717328868674603\n",
-      "2025-11-26 14:41:48,217 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.026717328868674603\n",
-      "2025-11-26 14:41:48,217 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.026907131992464417\n",
-      "2025-11-26 14:41:48,217 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.026907131992464417\n",
-      "2025-11-26 14:41:48,218 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.026891895663637772\n",
-      "2025-11-26 14:41:48,218 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.026891895663637772\n",
-      "2025-11-26 14:41:48,219 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.026904183153902535\n",
-      "2025-11-26 14:41:48,219 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.026904183153902535\n",
-      "2025-11-26 14:41:48,219 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.02670548992447055\n",
-      "2025-11-26 14:41:48,219 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.02670548992447055\n",
-      "2025-11-26 14:41:48,220 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
-      "2025-11-26 14:41:48,220 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n"
+      "2026-05-24 22:50:29,069 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.02654409503437145\n",
+      "2026-05-24 22:50:29,070 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.02654594276053423\n",
+      "2026-05-24 22:50:29,070 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.026542940338281404\n",
+      "2026-05-24 22:50:29,070 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02648946673229832\n",
+      "2026-05-24 22:50:29,070 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.02654409503437145\n",
+      "2026-05-24 22:50:29,071 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.02656635679892986\n",
+      "2026-05-24 22:50:29,071 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.02656855679108294\n",
+      "2026-05-24 22:50:29,071 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.02654409503437145\n",
+      "2026-05-24 22:50:29,071 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02654409503437145\n",
+      "2026-05-24 22:50:29,072 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.02656635679892986\n",
+      "2026-05-24 22:50:29,072 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.02654409503437145\n",
+      "2026-05-24 22:50:29,072 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.02656855679108294\n",
+      "2026-05-24 22:50:29,072 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.026548646955098107\n",
+      "2026-05-24 22:50:29,073 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.02654409503437145\n",
+      "2026-05-24 22:50:29,073 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.02656855679108294\n",
+      "2026-05-24 22:50:29,073 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.026543466868069885\n",
+      "2026-05-24 22:50:29,073 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    2.4s remaining:    0.3s\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    2.6s finished\n"
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    3.2s finished\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating patterns from clusters:: 100%|██████████| 6/6 [00:00<00:00, 12.23it/s]\n",
-      "2025-11-26 14:41:48,714 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "2025-11-26 14:41:48,714 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|██████████| 5/5 [00:05<00:00,  1.15s/it]\n",
-      "2025-11-26 14:41:55,786 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "2025-11-26 14:41:55,786 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|██████████| 10/10 [00:00<00:00, 11444.21it/s]\n",
-      "Computing subpatterns:: 100%|██████████| 10/10 [00:00<00:00, 39.33it/s]\n",
-      "2025-11-26 14:41:56,045 - modisco-lite - INFO - - Extracting 433 negative seqlets\n",
-      "2025-11-26 14:41:56,045 - modisco-lite - INFO - - Extracting 433 negative seqlets\n",
-      "2025-11-26 14:41:56,048 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 433 seqlets\n",
-      "2025-11-26 14:41:56,048 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 433 seqlets\n"
+      "Generating patterns from clusters:: 100%|██████████| 6/6 [00:00<00:00, 13.75it/s]\n",
+      "2026-05-24 22:50:29,513 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|██████████| 6/6 [00:16<00:00,  2.79s/it]\n",
+      "2026-05-24 22:50:47,722 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|██████████| 16/16 [00:00<00:00, 17919.59it/s]\n",
+      "Computing subpatterns:: 100%|██████████| 11/11 [00:46<00:00,  4.27s/it]\n",
+      "2026-05-24 22:51:34,649 - modisco-lite - INFO - - Extracting 326 negative seqlets\n",
+      "2026-05-24 22:51:34,652 - modisco-lite - INFO - - Round 0: Generating coarse resolution affinity matrix for 326 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b71e92f838b4171a265add23966bce2",
+       "model_id": "73b4368c9a644bbab1908a686979c155",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/326 [00:00<?, ?it/s]"
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      "metadata": {},
@@ -3407,19 +3194,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:56,662 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 433 seqlets and 433 neighbors\n",
-      "2025-11-26 14:41:56,662 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 433 seqlets and 433 neighbors\n"
+      "2026-05-24 22:51:35,044 - modisco-lite - INFO - - Round 0: Generating fine resolution affinity matrix for 326 seqlets and 326 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a3cc608098004e13aa6d82fb0f35c788",
+       "model_id": "f5796709f9e24f3dbf786b948db35ecf",
        "version_major": 2,
        "version_minor": 0
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@@ -3428,12 +3214,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30e94abd82344e8d9ae324edf8b7168e",
+       "model_id": "30fdee8e39cb47609127e59a0422ef7a",
        "version_major": 2,
        "version_minor": 0
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      "metadata": {},
@@ -3443,12 +3229,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:41:58,913 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-26 14:41:58,913 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
-      "2025-11-26 14:41:59,168 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-26 14:41:59,168 - modisco-lite - INFO - - Round 0: Density adaptation\n",
-      "2025-11-26 14:41:59,753 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n",
-      "2025-11-26 14:41:59,753 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:51:35,989 - modisco-lite - INFO - - Round 0: Filtering seqlets by correlation\n",
+      "2026-05-24 22:51:36,155 - modisco-lite - INFO - - Round 0: Density adaptation\n",
+      "2026-05-24 22:51:36,565 - modisco-lite - INFO - - Round 0: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -3456,87 +3239,69 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    5.6s\n",
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      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:42:08,048 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.022431033825772127\n",
-      "2025-11-26 14:42:08,048 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.022431033825772127\n",
-      "2025-11-26 14:42:08,049 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.022440185504726012\n",
-      "2025-11-26 14:42:08,049 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.022440185504726012\n",
-      "2025-11-26 14:42:08,050 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,050 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,051 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02237878036520486\n",
-      "2025-11-26 14:42:08,051 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02237878036520486\n",
-      "2025-11-26 14:42:08,051 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,051 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,052 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.022434044691735433\n",
-      "2025-11-26 14:42:08,052 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.022434044691735433\n",
-      "2025-11-26 14:42:08,052 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.022437768850843184\n",
-      "2025-11-26 14:42:08,052 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.022437768850843184\n",
-      "2025-11-26 14:42:08,053 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.022348901266141846\n",
-      "2025-11-26 14:42:08,053 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.022348901266141846\n",
-      "2025-11-26 14:42:08,053 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02248885492309427\n",
-      "2025-11-26 14:42:08,053 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02248885492309427\n",
-      "2025-11-26 14:42:08,054 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,054 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,054 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,054 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,055 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.022441028401136754\n",
-      "2025-11-26 14:42:08,055 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.022441028401136754\n",
-      "2025-11-26 14:42:08,055 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.022441028401136754\n",
-      "2025-11-26 14:42:08,055 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.022441028401136754\n",
-      "2025-11-26 14:42:08,056 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,056 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,056 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.022414649620829077\n",
-      "2025-11-26 14:42:08,056 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.022414649620829077\n",
-      "2025-11-26 14:42:08,057 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,057 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.022487699892515003\n",
-      "2025-11-26 14:42:08,057 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n",
-      "2025-11-26 14:42:08,057 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
+      "2026-05-24 22:51:45,909 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.026298529986125245\n",
+      "2026-05-24 22:51:45,910 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.026090858884059022\n",
+      "2026-05-24 22:51:45,910 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.02611744362353419\n",
+      "2026-05-24 22:51:45,911 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.02620678621600527\n",
+      "2026-05-24 22:51:45,911 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.026259921644356744\n",
+      "2026-05-24 22:51:45,911 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.02630706644818262\n",
+      "2026-05-24 22:51:45,912 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.02621852053454157\n",
+      "2026-05-24 22:51:45,912 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.026278588323849397\n",
+      "2026-05-24 22:51:45,912 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.02638666381879122\n",
+      "2026-05-24 22:51:45,913 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.0263752976655199\n",
+      "2026-05-24 22:51:45,913 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.0263752976655199\n",
+      "2026-05-24 22:51:45,913 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.02630900173952632\n",
+      "2026-05-24 22:51:45,914 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.02630288248132287\n",
+      "2026-05-24 22:51:45,914 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.026168854721067093\n",
+      "2026-05-24 22:51:45,915 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.026263161274804173\n",
+      "2026-05-24 22:51:45,915 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.026290584517879103\n",
+      "2026-05-24 22:51:45,915 - modisco-lite - INFO - - Round 0: Generating patterns from clusters\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    8.2s finished\n"
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    9.3s finished\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating patterns from clusters:: 100%|██████████| 5/5 [00:00<00:00, 10.36it/s]\n",
-      "2025-11-26 14:42:08,544 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 357 seqlets\n",
-      "2025-11-26 14:42:08,544 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 357 seqlets\n"
+      "Generating patterns from clusters:: 100%|██████████| 6/6 [00:00<00:00, 17.34it/s]\n",
+      "2026-05-24 22:51:46,267 - modisco-lite - INFO - - Round 1: Generating coarse resolution affinity matrix for 289 seqlets\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45bf2454d7bc4181822b9aa6bbfd3fb9",
+       "model_id": "5d625a8d12db45d69bc7eb932cf6f05d",
        "version_major": 2,
        "version_minor": 0
       },
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@@ -3546,19 +3311,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:42:08,921 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 357 seqlets and 357 neighbors\n",
-      "2025-11-26 14:42:08,921 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 357 seqlets and 357 neighbors\n"
+      "2026-05-24 22:51:46,545 - modisco-lite - INFO - - Round 1: Generating fine resolution affinity matrix for 289 seqlets and 289 neighbors\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d05405c84866489985f06df4ad7195af",
+       "model_id": "3b40c925fc9f4a94870d9f6b24347e95",
        "version_major": 2,
        "version_minor": 0
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     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e24d4f79bc4424eb8746b9459f81834",
+       "model_id": "d9df52a5349c47a898630eb013949bc3",
        "version_major": 2,
        "version_minor": 0
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:42:11,332 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-26 14:42:11,332 - modisco-lite - INFO - - Round 1: Density adaptation\n",
-      "2025-11-26 14:42:11,842 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n",
-      "2025-11-26 14:42:11,842 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
+      "2026-05-24 22:51:47,687 - modisco-lite - INFO - - Round 1: Density adaptation\n",
+      "2026-05-24 22:51:48,107 - modisco-lite - INFO - - Round 1: Clustering with Leiden algorithm\n"
      ]
     },
     {
@@ -3593,69 +3355,62 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    0.7s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    0.7s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    0.8s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    1.2s\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    1.4s\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    1.5s\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    1.7s\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    1.9s\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    2.2s\n",
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-      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    3.2s finished\n"
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    0.4s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    0.4s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    0.4s\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    0.6s\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    0.9s\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    1.0s\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    1.0s\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    1.0s\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    1.3s\n",
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      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-11-26 14:42:15,051 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,051 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,052 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.027173510111888555\n",
-      "2025-11-26 14:42:15,052 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.027173510111888555\n",
-      "2025-11-26 14:42:15,053 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.027124237754638427\n",
-      "2025-11-26 14:42:15,053 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.027124237754638427\n",
-      "2025-11-26 14:42:15,053 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.027113717367478456\n",
-      "2025-11-26 14:42:15,053 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.027113717367478456\n",
-      "2025-11-26 14:42:15,054 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,054 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,054 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,054 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,054 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.027111849183321615\n",
-      "2025-11-26 14:42:15,054 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.027111849183321615\n",
-      "2025-11-26 14:42:15,055 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.027111849183321615\n",
-      "2025-11-26 14:42:15,055 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.027111849183321615\n",
-      "2025-11-26 14:42:15,056 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.025788956117218736\n",
-      "2025-11-26 14:42:15,056 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.025788956117218736\n",
-      "2025-11-26 14:42:15,056 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.027111849183321615\n",
-      "2025-11-26 14:42:15,056 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.027111849183321615\n",
-      "2025-11-26 14:42:15,057 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.027173510111888555\n",
-      "2025-11-26 14:42:15,057 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.027173510111888555\n",
-      "2025-11-26 14:42:15,057 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.027114592541273685\n",
-      "2025-11-26 14:42:15,057 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.027114592541273685\n",
-      "2025-11-26 14:42:15,058 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.027099803327968703\n",
-      "2025-11-26 14:42:15,058 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.027099803327968703\n",
-      "2025-11-26 14:42:15,058 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.027173510111888555\n",
-      "2025-11-26 14:42:15,058 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.027173510111888555\n",
-      "2025-11-26 14:42:15,058 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,058 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,059 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,059 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.027130644671945807\n",
-      "2025-11-26 14:42:15,059 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
-      "2025-11-26 14:42:15,059 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n",
-      "Generating patterns from clusters:: 100%|██████████| 5/5 [00:00<00:00, 10.65it/s]\n",
-      "2025-11-26 14:42:15,533 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "2025-11-26 14:42:15,533 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
-      "Detecting spurious merging of patterns:: 100%|██████████| 5/5 [00:06<00:00,  1.33s/it]\n",
-      "2025-11-26 14:42:25,798 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "2025-11-26 14:42:25,798 - modisco-lite - INFO - Filtering and merging patterns\n",
-      "Filtering patterns:: 100%|██████████| 17/17 [00:00<00:00, 24211.60it/s]\n",
-      "Computing subpatterns:: 100%|██████████| 8/8 [00:00<00:00, 46.04it/s]\n"
+      "2026-05-24 22:51:50,084 - modisco-lite - INFO - Leiden clustering quality for seed 0: 0.030878386050972168\n",
+      "2026-05-24 22:51:50,084 - modisco-lite - INFO - Leiden clustering quality for seed 1: 0.030881838489324555\n",
+      "2026-05-24 22:51:50,085 - modisco-lite - INFO - Leiden clustering quality for seed 2: 0.03097236475133878\n",
+      "2026-05-24 22:51:50,085 - modisco-lite - INFO - Leiden clustering quality for seed 3: 0.030969097092347273\n",
+      "2026-05-24 22:51:50,085 - modisco-lite - INFO - Leiden clustering quality for seed 4: 0.031116717626987464\n",
+      "2026-05-24 22:51:50,086 - modisco-lite - INFO - Leiden clustering quality for seed 5: 0.030969097092347273\n",
+      "2026-05-24 22:51:50,086 - modisco-lite - INFO - Leiden clustering quality for seed 6: 0.031106394985342987\n",
+      "2026-05-24 22:51:50,086 - modisco-lite - INFO - Leiden clustering quality for seed 7: 0.030915599342898194\n",
+      "2026-05-24 22:51:50,087 - modisco-lite - INFO - Leiden clustering quality for seed 8: 0.030956105128683888\n",
+      "2026-05-24 22:51:50,087 - modisco-lite - INFO - Leiden clustering quality for seed 9: 0.031102844366169467\n",
+      "2026-05-24 22:51:50,088 - modisco-lite - INFO - Leiden clustering quality for seed 10: 0.03095378900112343\n",
+      "2026-05-24 22:51:50,088 - modisco-lite - INFO - Leiden clustering quality for seed 11: 0.031116717626987464\n",
+      "2026-05-24 22:51:50,088 - modisco-lite - INFO - Leiden clustering quality for seed 12: 0.031132004183524385\n",
+      "2026-05-24 22:51:50,089 - modisco-lite - INFO - Leiden clustering quality for seed 13: 0.031132004183524385\n",
+      "2026-05-24 22:51:50,089 - modisco-lite - INFO - Leiden clustering quality for seed 14: 0.031128293465430234\n",
+      "2026-05-24 22:51:50,089 - modisco-lite - INFO - Leiden clustering quality for seed 15: 0.031094997916790025\n",
+      "2026-05-24 22:51:50,090 - modisco-lite - INFO - - Round 1: Generating patterns from clusters\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=4)]: Done  13 out of  16 | elapsed:    1.9s remaining:    0.4s\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  16 | elapsed:    1.9s remaining:    0.3s\n",
+      "[Parallel(n_jobs=4)]: Done  16 out of  16 | elapsed:    2.0s finished\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating patterns from clusters:: 100%|██████████| 5/5 [00:00<00:00, 15.55it/s]\n",
+      "2026-05-24 22:51:50,414 - modisco-lite - INFO - Detecting spurious merging of patterns\n",
+      "Detecting spurious merging of patterns:: 100%|██████████| 5/5 [00:18<00:00,  3.76s/it]\n",
+      "2026-05-24 22:52:10,298 - modisco-lite - INFO - Filtering and merging patterns\n",
+      "Filtering patterns:: 100%|██████████| 14/14 [00:00<00:00, 21748.24it/s]\n",
+      "Computing subpatterns:: 100%|██████████| 6/6 [00:25<00:00,  4.25s/it]\n"
      ]
     }
    ],
@@ -3679,31 +3434,24 @@
    "id": "93509cab",
    "metadata": {},
    "source": [
-    "`modisco_reports` runs tomtom and annotated patterns with now motifs."
+    "`modisco_reports` runs tomtom and annotates patterns with motifs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "4da0140a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:16:09.302780Z",
-     "iopub.status.busy": "2025-11-21T06:16:09.302630Z",
-     "iopub.status.idle": "2025-11-21T06:16:09.518690Z",
-     "shell.execute_reply": "2025-11-21T06:16:09.517606Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Creating modisco logos for pos_patterns: 100%|██████████| 10/10 [00:19<00:00,  1.91s/it]\n",
-      "Creating modisco logos for neg_patterns: 100%|██████████| 8/8 [00:18<00:00,  2.28s/it]\n",
-      "Generating patterns dataframe: 100%|██████████| 2/2 [00:00<00:00, 414.64it/s]\n",
-      "Reading patterns for pos_patterns: 100%|██████████| 10/10 [00:00<00:00, 3074.10it/s]\n",
-      "Reading patterns for neg_patterns: 100%|██████████| 8/8 [00:00<00:00, 3027.29it/s]\n"
+      "Creating modisco logos for pos_patterns: 100%|██████████| 11/11 [00:19<00:00,  1.80s/it]\n",
+      "Creating modisco logos for neg_patterns: 100%|██████████| 6/6 [00:15<00:00,  2.60s/it]\n",
+      "Generating patterns dataframe: 100%|██████████| 2/2 [00:00<00:00, 446.04it/s]\n",
+      "Reading patterns for pos_patterns: 100%|██████████| 11/11 [00:00<00:00, 3065.81it/s]\n",
+      "Reading patterns for neg_patterns: 100%|██████████| 6/6 [00:00<00:00, 2972.22it/s]\n"
      ]
     },
     {
@@ -3711,23 +3459,22 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    6.1s\n",
-      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:   15.5s\n",
-      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:   18.4s\n",
-      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:  1.8min\n",
-      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:  2.3min\n",
-      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:  2.3min\n",
-      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:  2.3min\n",
-      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:  3.7min\n",
-      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:  3.7min\n",
-      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:  4.2min\n",
-      "[Parallel(n_jobs=4)]: Done  11 tasks      | elapsed:  4.3min\n",
-      "[Parallel(n_jobs=4)]: Done  12 out of  18 | elapsed:  4.6min remaining:  2.3min\n",
-      "[Parallel(n_jobs=4)]: Done  13 out of  18 | elapsed:  5.6min remaining:  2.2min\n",
-      "[Parallel(n_jobs=4)]: Done  14 out of  18 | elapsed:  5.6min remaining:  1.6min\n",
-      "[Parallel(n_jobs=4)]: Done  15 out of  18 | elapsed:  5.6min remaining:  1.1min\n",
-      "[Parallel(n_jobs=4)]: Done  16 out of  18 | elapsed:  6.0min remaining:   45.0s\n",
-      "[Parallel(n_jobs=4)]: Done  18 out of  18 | elapsed:  6.5min finished\n"
+      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    7.0s\n",
+      "[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:   36.2s\n",
+      "[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:  1.2min\n",
+      "[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:  1.3min\n",
+      "[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:  1.4min\n",
+      "[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:  1.5min\n",
+      "[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:  1.9min\n",
+      "[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:  1.9min\n",
+      "[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:  2.2min\n",
+      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:  2.7min\n",
+      "[Parallel(n_jobs=4)]: Done  11 out of  17 | elapsed:  2.7min remaining:  1.5min\n",
+      "[Parallel(n_jobs=4)]: Done  12 out of  17 | elapsed:  2.9min remaining:  1.2min\n",
+      "[Parallel(n_jobs=4)]: Done  13 out of  17 | elapsed:  3.3min remaining:  1.0min\n",
+      "[Parallel(n_jobs=4)]: Done  14 out of  17 | elapsed:  3.6min remaining:   46.6s\n",
+      "[Parallel(n_jobs=4)]: Done  15 out of  17 | elapsed:  3.9min remaining:   31.2s\n",
+      "[Parallel(n_jobs=4)]: Done  17 out of  17 | elapsed:  4.6min finished\n"
      ]
     }
    ],
@@ -3743,31 +3490,21 @@
    "id": "73eb8989",
    "metadata": {},
    "source": [
-    "Seqlet bed extract seqlets from modisco output:"
+    "`modisco_seqlet_bed` extracts seqlets from modisco output:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "e9d35b2b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:16:09.520417Z",
-     "iopub.status.busy": "2025-11-21T06:16:09.520268Z",
-     "iopub.status.idle": "2025-11-21T06:16:09.620305Z",
-     "shell.execute_reply": "2025-11-21T06:16:09.619489Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
-      "Done. 00:00:01.7 (1847.4MB/s)\n",
-      "Processing pos_patterns patterns...: 100%|██████████| 10/10 [00:00<00:00, 456.53it/s]\n",
-      "Processing neg_patterns patterns...: 100%|██████████| 8/8 [00:00<00:00, 525.26it/s]\n"
+      "Processing pos_patterns patterns...: 100%|██████████| 11/11 [00:00<00:00, 591.41it/s]\n",
+      "Processing neg_patterns patterns...: 100%|██████████| 6/6 [00:00<00:00, 599.17it/s]\n"
      ]
     }
    ],
@@ -3777,13 +3514,21 @@
     "    modisco_h5=\"example/modisco_python_subcommands/modisco_fibroblast.modisco.h5\",\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5e1dca4-1cb9-4ba2-9569-dc64bd1fe1f0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "decima2",
+   "display_name": "Python (decima)",
    "language": "python",
-   "name": "python3"
+   "name": "decima"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3795,7 +3540,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.15"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/docs/tutorials/5-gene-expression-prediction.ipynb b/docs/tutorials/5-gene-expression-prediction.ipynb
index 9aa599c..6c502a0 100644
--- a/docs/tutorials/5-gene-expression-prediction.ipynb
+++ b/docs/tutorials/5-gene-expression-prediction.ipynb
@@ -33,60 +33,15 @@
    "id": "f4634c23",
    "metadata": {},
    "source": [
-    "Scores for all genes in the training data are precomputed and saved to metadata h5ad object for each model replicate and are available under the `DecimaResult` class. `predicted_expression_matrix` class returns predicted average gene expression across the replicates."
+    "Predicted expression levels (log (CPM + 1)) for all genes in the training data have been precomputed and saved to the metadata h5ad object for each model replicate and are available under the `DecimaResult` class. The `predicted_expression_matrix` class returns the predicted average gene expression across the replicates."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "6b76abce",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:24:54.170009Z",
-     "iopub.status.busy": "2025-11-21T06:24:54.169485Z",
-     "iopub.status.idle": "2025-11-21T06:25:32.182146Z",
-     "shell.execute_reply": "2025-11-21T06:25:32.181240Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
-      "  warnings.warn(\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:08.3 (375.3MB/s)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -481,14 +436,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "acbb140a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:25:32.184513Z",
-     "iopub.status.busy": "2025-11-21T06:25:32.183699Z",
-     "iopub.status.idle": "2025-11-21T06:25:32.199071Z",
-     "shell.execute_reply": "2025-11-21T06:25:32.198367Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -881,14 +829,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "95ebbd2f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:25:32.200797Z",
-     "iopub.status.busy": "2025-11-21T06:25:32.200269Z",
-     "iopub.status.idle": "2025-11-21T06:25:32.214579Z",
-     "shell.execute_reply": "2025-11-21T06:25:32.213871Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1273,14 +1214,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "3435f5fc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:25:32.216226Z",
-     "iopub.status.busy": "2025-11-21T06:25:32.215688Z",
-     "iopub.status.idle": "2025-11-21T06:25:32.219914Z",
-     "shell.execute_reply": "2025-11-21T06:25:32.219213Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1299,265 +1233,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "345ff486",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "source": [
-    "### CLI API"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "86e9f055",
+   "id": "de5bfe6d",
    "metadata": {},
    "source": [
-    "If you want to perform gene expression prediction again, rather than using the precomputed scores, you can use the Decima command-line interface (CLI) to generate new predictions for any set of genes you specify. For example, you can run the `decima predict-genes` command with the `--genes` argument to provide a comma-separated list of gene names (such as \"STRADA,ETV4,USP25\") if no gene provided it will perform expression predictions for all genes, select the prediction model with the `--model` option (for instance, \"ensemble\" or a specific replicate like \"0\"), and use `--save-replicates` to save predictions for each replicate. The `-o` flag lets you specify the output file path for the predictions in `.h5ad` format. "
+    "Remember, to see the metadata for the various output tracks:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "6715257c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:25:32.221652Z",
-     "iopub.status.busy": "2025-11-21T06:25:32.221116Z",
-     "iopub.status.idle": "2025-11-21T06:26:16.929651Z",
-     "shell.execute_reply": "2025-11-21T06:26:16.928993Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n",
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\r\n",
-      "  warnings.warn(\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "decima - INFO - Using device: 0 and genome: hg38 for prediction.\r\n",
-      "decima - INFO - Loading model ensemble...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:02.6 (272.9MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep1:latest', 720.03MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:01.8 (405.6MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep2:latest', 720.03MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:02.0 (359.5MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep3:latest', 720.03MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:01.8 (400.1MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "decima - INFO - Making predictions\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:01.9 (1645.5MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)\r\n",
-      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\r\n",
-      "TPU available: False, using: 0 TPU cores\r\n",
-      "HPU available: False, using: 0 HPUs\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SLURM auto-requeueing enabled. Setting signal handlers.\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Predicting: |                                             | 0/? [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Predicting: |                                             | 0/? [00:00<?, ?it/s]\r",
-      "Predicting DataLoader 0:   0%|                            | 0/3 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Predicting DataLoader 0:  33%|██████▋             | 1/3 [00:00<00:01,  1.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Predicting DataLoader 0:  67%|█████████████▎      | 2/3 [00:01<00:00,  1.67it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Predicting DataLoader 0: 100%|████████████████████| 3/3 [00:01<00:00,  1.82it/s]\r",
-      "Predicting DataLoader 0: 100%|████████████████████| 3/3 [00:01<00:00,  1.81it/s]\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\r\n",
-      "decima - INFO - Creating anndata\r\n",
-      "decima - INFO - Evaluating performance\r\n",
-      "decima - WARNING - No ground truth expression matrix found in the metadata. Skipping evaluation.\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "! decima predict-genes --genes \"STRADA,ETV4,USP25\" --model ensemble --save-replicates -o example/predict_genes/predictions.h5ad "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4d2c0356",
+   "id": "c7dfd78e",
    "metadata": {},
-   "source": [
-    "After running this command, you can load the resulting predictions in Python using the `DecimaResult` class and access the predicted expression matrix as:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e598b85c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:16.931282Z",
-     "iopub.status.busy": "2025-11-21T06:26:16.931107Z",
-     "iopub.status.idle": "2025-11-21T06:26:16.979662Z",
-     "shell.execute_reply": "2025-11-21T06:26:16.979173Z"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1580,77 +1266,490 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>STRADA</th>\n",
-       "      <th>ETV4</th>\n",
-       "      <th>USP25</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>organ</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>study</th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>region</th>\n",
+       "      <th>subregion</th>\n",
+       "      <th>celltype_coarse</th>\n",
+       "      <th>n_cells</th>\n",
+       "      <th>total_counts</th>\n",
+       "      <th>n_genes</th>\n",
+       "      <th>size_factor</th>\n",
+       "      <th>train_pearson</th>\n",
+       "      <th>val_pearson</th>\n",
+       "      <th>test_pearson</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>agg_0</th>\n",
-       "      <td>3.060561</td>\n",
-       "      <td>2.882100</td>\n",
-       "      <td>3.469085</td>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Amygdala</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>jhpce#tran2021</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>331</td>\n",
+       "      <td>1.592883e+07</td>\n",
+       "      <td>17000</td>\n",
+       "      <td>41431.465186</td>\n",
+       "      <td>0.942459</td>\n",
+       "      <td>0.841377</td>\n",
+       "      <td>0.865640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_1</th>\n",
-       "      <td>3.117702</td>\n",
-       "      <td>2.825738</td>\n",
-       "      <td>3.596372</td>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Basolateral nuclear group (BLN) - lat...</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Basolateral nuclear group (BLN) - lateral nucl...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11369</td>\n",
+       "      <td>2.952133e+08</td>\n",
+       "      <td>18080</td>\n",
+       "      <td>40765.341481</td>\n",
+       "      <td>0.943098</td>\n",
+       "      <td>0.838936</td>\n",
+       "      <td>0.861092</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_2</th>\n",
-       "      <td>3.156642</td>\n",
-       "      <td>3.122381</td>\n",
-       "      <td>3.718749</td>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Bed nucleus of stria terminalis and n...</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Bed nucleus of stria terminalis and nearby - BNST</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>139</td>\n",
+       "      <td>2.593231e+06</td>\n",
+       "      <td>15418</td>\n",
+       "      <td>42556.387020</td>\n",
+       "      <td>0.952170</td>\n",
+       "      <td>0.854544</td>\n",
+       "      <td>0.866654</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_3</th>\n",
-       "      <td>3.214047</td>\n",
-       "      <td>3.204670</td>\n",
-       "      <td>3.629874</td>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Central nuclear group - CEN</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Central nuclear group - CEN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3892</td>\n",
+       "      <td>9.946371e+07</td>\n",
+       "      <td>17959</td>\n",
+       "      <td>42884.641430</td>\n",
+       "      <td>0.959744</td>\n",
+       "      <td>0.863585</td>\n",
+       "      <td>0.881554</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_4</th>\n",
-       "      <td>3.103570</td>\n",
-       "      <td>3.032028</td>\n",
-       "      <td>3.512117</td>\n",
+       "      <td>Amygdala excitatory</td>\n",
+       "      <td>Amygdala_Corticomedial nuclear group (CMN) - a...</td>\n",
+       "      <td>CNS</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>SCR_016152</td>\n",
+       "      <td>brain_atlas</td>\n",
+       "      <td>Amygdala</td>\n",
+       "      <td>Corticomedial nuclear group (CMN) - anterior c...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2945</td>\n",
+       "      <td>1.281619e+08</td>\n",
+       "      <td>17885</td>\n",
+       "      <td>41816.741933</td>\n",
+       "      <td>0.951365</td>\n",
+       "      <td>0.854304</td>\n",
+       "      <td>0.868902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9533</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>upper lobe of right lung</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3.483375e+04</td>\n",
+       "      <td>8515</td>\n",
+       "      <td>35404.911768</td>\n",
+       "      <td>0.735213</td>\n",
+       "      <td>0.665647</td>\n",
+       "      <td>0.654491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9535</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>urinary bladder</td>\n",
+       "      <td>urinary</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>GSE129845</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8.498500e+04</td>\n",
+       "      <td>7337</td>\n",
+       "      <td>26189.415789</td>\n",
+       "      <td>0.809852</td>\n",
+       "      <td>0.690022</td>\n",
+       "      <td>0.656160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9536</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>272</td>\n",
+       "      <td>5.700762e+05</td>\n",
+       "      <td>14769</td>\n",
+       "      <td>44938.403867</td>\n",
+       "      <td>0.915329</td>\n",
+       "      <td>0.808941</td>\n",
+       "      <td>0.839993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9537</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>uterus</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>472</td>\n",
+       "      <td>1.089170e+07</td>\n",
+       "      <td>14514</td>\n",
+       "      <td>30145.422152</td>\n",
+       "      <td>0.852339</td>\n",
+       "      <td>0.717682</td>\n",
+       "      <td>0.727469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>agg_9533</th>\n",
-       "      <td>2.313220</td>\n",
-       "      <td>2.173597</td>\n",
-       "      <td>3.156157</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>agg_9535</th>\n",
-       "      <td>0.952112</td>\n",
-       "      <td>0.956720</td>\n",
-       "      <td>1.250005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>agg_9536</th>\n",
-       "      <td>2.779494</td>\n",
-       "      <td>2.705945</td>\n",
-       "      <td>3.530610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>agg_9537</th>\n",
-       "      <td>1.342694</td>\n",
-       "      <td>1.407407</td>\n",
-       "      <td>1.867070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>agg_9538</th>\n",
-       "      <td>1.745080</td>\n",
-       "      <td>1.633314</td>\n",
-       "      <td>2.082985</td>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>vasculature</td>\n",
+       "      <td>vasculature</td>\n",
+       "      <td>healthy</td>\n",
+       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
+       "      <td>scimilarity</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>5.992697e+07</td>\n",
+       "      <td>16764</td>\n",
+       "      <td>36464.273371</td>\n",
+       "      <td>0.909855</td>\n",
+       "      <td>0.780413</td>\n",
+       "      <td>0.796351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8856 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       cell_type  \\\n",
+       "agg_0                        Amygdala excitatory   \n",
+       "agg_1                        Amygdala excitatory   \n",
+       "agg_2                        Amygdala excitatory   \n",
+       "agg_3                        Amygdala excitatory   \n",
+       "agg_4                        Amygdala excitatory   \n",
+       "...                                          ...   \n",
+       "agg_9533  vascular associated smooth muscle cell   \n",
+       "agg_9535  vascular associated smooth muscle cell   \n",
+       "agg_9536  vascular associated smooth muscle cell   \n",
+       "agg_9537  vascular associated smooth muscle cell   \n",
+       "agg_9538  vascular associated smooth muscle cell   \n",
+       "\n",
+       "                                                     tissue        organ  \\\n",
+       "agg_0                                     Amygdala_Amygdala          CNS   \n",
+       "agg_1     Amygdala_Basolateral nuclear group (BLN) - lat...          CNS   \n",
+       "agg_2     Amygdala_Bed nucleus of stria terminalis and n...          CNS   \n",
+       "agg_3                  Amygdala_Central nuclear group - CEN          CNS   \n",
+       "agg_4     Amygdala_Corticomedial nuclear group (CMN) - a...          CNS   \n",
+       "...                                                     ...          ...   \n",
+       "agg_9533                           upper lobe of right lung         lung   \n",
+       "agg_9535                                    urinary bladder      urinary   \n",
+       "agg_9536                                             uterus       uterus   \n",
+       "agg_9537                                             uterus       uterus   \n",
+       "agg_9538                                        vasculature  vasculature   \n",
+       "\n",
+       "          disease                                 study      dataset  \\\n",
+       "agg_0     healthy                        jhpce#tran2021  brain_atlas   \n",
+       "agg_1     healthy                            SCR_016152  brain_atlas   \n",
+       "agg_2     healthy                            SCR_016152  brain_atlas   \n",
+       "agg_3     healthy                            SCR_016152  brain_atlas   \n",
+       "agg_4     healthy                            SCR_016152  brain_atlas   \n",
+       "...           ...                                   ...          ...   \n",
+       "agg_9533       NA                                ENCODE  scimilarity   \n",
+       "agg_9535  healthy                             GSE129845  scimilarity   \n",
+       "agg_9536       NA                                ENCODE  scimilarity   \n",
+       "agg_9537  healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity   \n",
+       "agg_9538  healthy  e5f58829-1a66-40b5-a624-9046778e74f5  scimilarity   \n",
+       "\n",
+       "            region                                          subregion  \\\n",
+       "agg_0     Amygdala                                           Amygdala   \n",
+       "agg_1     Amygdala  Basolateral nuclear group (BLN) - lateral nucl...   \n",
+       "agg_2     Amygdala  Bed nucleus of stria terminalis and nearby - BNST   \n",
+       "agg_3     Amygdala                        Central nuclear group - CEN   \n",
+       "agg_4     Amygdala  Corticomedial nuclear group (CMN) - anterior c...   \n",
+       "...            ...                                                ...   \n",
+       "agg_9533       nan                                                nan   \n",
+       "agg_9535       nan                                                nan   \n",
+       "agg_9536       nan                                                nan   \n",
+       "agg_9537       nan                                                nan   \n",
+       "agg_9538       nan                                                nan   \n",
+       "\n",
+       "         celltype_coarse  n_cells  total_counts  n_genes   size_factor  \\\n",
+       "agg_0                NaN      331  1.592883e+07    17000  41431.465186   \n",
+       "agg_1                NaN    11369  2.952133e+08    18080  40765.341481   \n",
+       "agg_2                NaN      139  2.593231e+06    15418  42556.387020   \n",
+       "agg_3                NaN     3892  9.946371e+07    17959  42884.641430   \n",
+       "agg_4                NaN     2945  1.281619e+08    17885  41816.741933   \n",
+       "...                  ...      ...           ...      ...           ...   \n",
+       "agg_9533             NaN       21  3.483375e+04     8515  35404.911768   \n",
+       "agg_9535             NaN       24  8.498500e+04     7337  26189.415789   \n",
+       "agg_9536             NaN      272  5.700762e+05    14769  44938.403867   \n",
+       "agg_9537             NaN      472  1.089170e+07    14514  30145.422152   \n",
+       "agg_9538             NaN     1853  5.992697e+07    16764  36464.273371   \n",
+       "\n",
+       "          train_pearson  val_pearson  test_pearson  \n",
+       "agg_0          0.942459     0.841377      0.865640  \n",
+       "agg_1          0.943098     0.838936      0.861092  \n",
+       "agg_2          0.952170     0.854544      0.866654  \n",
+       "agg_3          0.959744     0.863585      0.881554  \n",
+       "agg_4          0.951365     0.854304      0.868902  \n",
+       "...                 ...          ...           ...  \n",
+       "agg_9533       0.735213     0.665647      0.654491  \n",
+       "agg_9535       0.809852     0.690022      0.656160  \n",
+       "agg_9536       0.915329     0.808941      0.839993  \n",
+       "agg_9537       0.852339     0.717682      0.727469  \n",
+       "agg_9538       0.909855     0.780413      0.796351  \n",
+       "\n",
+       "[8856 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.cell_metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "345ff486",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "### CLI API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86e9f055",
+   "metadata": {},
+   "source": [
+    "If you want to perform gene expression prediction again, rather than using the precomputed scores, you can use the Decima command-line interface (CLI) to generate new predictions for any set of genes you specify. For example, you can run the `decima predict-genes` command with the `--genes` argument to provide a comma-separated list of gene names (such as \"STRADA,ETV4,USP25\") if no gene provided it will perform expression predictions for all genes, select the prediction model with the `--model` option (for instance, \"ensemble\" or a specific replicate like \"0\"), and use `--save-replicates` to save predictions for each replicate. The `-o` flag lets you specify the output file path for the predictions in `.h5ad` format. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6715257c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "decima - INFO - Using device: 0 and genome: hg38 for prediction.\n",
+      "decima - INFO - Loading model ensemble...\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "rep2.safetensors: 100%|██████████████████████| 755M/755M [00:08<00:00, 85.4MB/s]\n",
+      "rep3.safetensors: 100%|██████████████████████| 755M/755M [00:07<00:00, 99.0MB/s]\n",
+      "decima - INFO - Making predictions\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n",
+      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n",
+      "\u001b[2KPredicting \u001b[35m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3/3 \u001b[2m0:00:01 • 0:00:00\u001b[0m \u001b[2;4m2.24it/s\u001b[0m [2;4m2.24it/s\u001b[0m \n",
+      "\u001b[?25h/home/lala8/miniforge3/envs/decima/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n",
+      "decima - INFO - Creating anndata\n",
+      "decima - INFO - Evaluating performance\n",
+      "decima - WARNING - No ground truth expression matrix found in the metadata. Skipping evaluation.\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "! decima predict-genes --genes \"STRADA,ETV4,USP25\" --model ensemble --save-replicates -o example/predict_genes/predictions.h5ad "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d2c0356",
+   "metadata": {},
+   "source": [
+    "After running this command, you can load the resulting predictions in Python using the `DecimaResult` class and access the predicted expression matrix as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e598b85c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STRADA</th>\n",
+       "      <th>ETV4</th>\n",
+       "      <th>USP25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>agg_0</th>\n",
+       "      <td>3.060397</td>\n",
+       "      <td>2.881919</td>\n",
+       "      <td>3.469221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_1</th>\n",
+       "      <td>3.117476</td>\n",
+       "      <td>2.825525</td>\n",
+       "      <td>3.596472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_2</th>\n",
+       "      <td>3.156399</td>\n",
+       "      <td>3.122201</td>\n",
+       "      <td>3.718817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_3</th>\n",
+       "      <td>3.213858</td>\n",
+       "      <td>3.204461</td>\n",
+       "      <td>3.629969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_4</th>\n",
+       "      <td>3.103418</td>\n",
+       "      <td>3.031820</td>\n",
+       "      <td>3.512249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9533</th>\n",
+       "      <td>2.313128</td>\n",
+       "      <td>2.173468</td>\n",
+       "      <td>3.156261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9535</th>\n",
+       "      <td>0.952004</td>\n",
+       "      <td>0.956674</td>\n",
+       "      <td>1.250045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9536</th>\n",
+       "      <td>2.779305</td>\n",
+       "      <td>2.705866</td>\n",
+       "      <td>3.530668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9537</th>\n",
+       "      <td>1.342528</td>\n",
+       "      <td>1.407362</td>\n",
+       "      <td>1.867149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_9538</th>\n",
+       "      <td>1.744911</td>\n",
+       "      <td>1.633233</td>\n",
+       "      <td>2.083091</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1659,22 +1758,22 @@
       ],
       "text/plain": [
        "            STRADA      ETV4     USP25\n",
-       "agg_0     3.060561  2.882100  3.469085\n",
-       "agg_1     3.117702  2.825738  3.596372\n",
-       "agg_2     3.156642  3.122381  3.718749\n",
-       "agg_3     3.214047  3.204670  3.629874\n",
-       "agg_4     3.103570  3.032028  3.512117\n",
+       "agg_0     3.060397  2.881919  3.469221\n",
+       "agg_1     3.117476  2.825525  3.596472\n",
+       "agg_2     3.156399  3.122201  3.718817\n",
+       "agg_3     3.213858  3.204461  3.629969\n",
+       "agg_4     3.103418  3.031820  3.512249\n",
        "...            ...       ...       ...\n",
-       "agg_9533  2.313220  2.173597  3.156157\n",
-       "agg_9535  0.952112  0.956720  1.250005\n",
-       "agg_9536  2.779494  2.705945  3.530610\n",
-       "agg_9537  1.342694  1.407407  1.867070\n",
-       "agg_9538  1.745080  1.633314  2.082985\n",
+       "agg_9533  2.313128  2.173468  3.156261\n",
+       "agg_9535  0.952004  0.956674  1.250045\n",
+       "agg_9536  2.779305  2.705866  3.530668\n",
+       "agg_9537  1.342528  1.407362  1.867149\n",
+       "agg_9538  1.744911  1.633233  2.083091\n",
        "\n",
        "[8856 rows x 3 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1694,16 +1793,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "ce3884f4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:16.981009Z",
-     "iopub.status.busy": "2025-11-21T06:26:16.980750Z",
-     "iopub.status.idle": "2025-11-21T06:26:16.986183Z",
-     "shell.execute_reply": "2025-11-21T06:26:16.985726Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1734,33 +1826,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>agg_0</th>\n",
-       "      <td>2.933017</td>\n",
-       "      <td>2.892934</td>\n",
-       "      <td>2.858879</td>\n",
+       "      <td>2.932824</td>\n",
+       "      <td>2.892707</td>\n",
+       "      <td>2.858933</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_1</th>\n",
-       "      <td>2.816778</td>\n",
-       "      <td>2.812964</td>\n",
-       "      <td>3.058485</td>\n",
+       "      <td>2.816676</td>\n",
+       "      <td>2.812716</td>\n",
+       "      <td>3.058435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_2</th>\n",
-       "      <td>2.743120</td>\n",
-       "      <td>2.971366</td>\n",
-       "      <td>2.950323</td>\n",
+       "      <td>2.742964</td>\n",
+       "      <td>2.971174</td>\n",
+       "      <td>2.950304</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_3</th>\n",
-       "      <td>2.804692</td>\n",
-       "      <td>3.346689</td>\n",
-       "      <td>2.837382</td>\n",
+       "      <td>2.804557</td>\n",
+       "      <td>3.346401</td>\n",
+       "      <td>2.837452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_4</th>\n",
-       "      <td>2.816030</td>\n",
-       "      <td>3.088620</td>\n",
-       "      <td>2.973081</td>\n",
+       "      <td>2.815957</td>\n",
+       "      <td>3.088338</td>\n",
+       "      <td>2.973126</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1770,33 +1862,33 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_9533</th>\n",
-       "      <td>2.510605</td>\n",
-       "      <td>2.760446</td>\n",
-       "      <td>1.965776</td>\n",
+       "      <td>2.510271</td>\n",
+       "      <td>2.760181</td>\n",
+       "      <td>1.965718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_9535</th>\n",
-       "      <td>1.246022</td>\n",
-       "      <td>1.192524</td>\n",
-       "      <td>0.407104</td>\n",
+       "      <td>1.245795</td>\n",
+       "      <td>1.192324</td>\n",
+       "      <td>0.407210</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_9536</th>\n",
-       "      <td>2.809229</td>\n",
-       "      <td>3.369957</td>\n",
-       "      <td>2.927355</td>\n",
+       "      <td>2.808904</td>\n",
+       "      <td>3.369778</td>\n",
+       "      <td>2.927307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_9537</th>\n",
-       "      <td>1.580334</td>\n",
-       "      <td>1.533467</td>\n",
-       "      <td>0.879179</td>\n",
+       "      <td>1.580065</td>\n",
+       "      <td>1.533221</td>\n",
+       "      <td>0.879286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>agg_9538</th>\n",
-       "      <td>2.014224</td>\n",
-       "      <td>2.078125</td>\n",
-       "      <td>1.374325</td>\n",
+       "      <td>2.013909</td>\n",
+       "      <td>2.077899</td>\n",
+       "      <td>1.374621</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1805,22 +1897,22 @@
       ],
       "text/plain": [
        "            STRADA      ETV4     USP25\n",
-       "agg_0     2.933017  2.892934  2.858879\n",
-       "agg_1     2.816778  2.812964  3.058485\n",
-       "agg_2     2.743120  2.971366  2.950323\n",
-       "agg_3     2.804692  3.346689  2.837382\n",
-       "agg_4     2.816030  3.088620  2.973081\n",
+       "agg_0     2.932824  2.892707  2.858933\n",
+       "agg_1     2.816676  2.812716  3.058435\n",
+       "agg_2     2.742964  2.971174  2.950304\n",
+       "agg_3     2.804557  3.346401  2.837452\n",
+       "agg_4     2.815957  3.088338  2.973126\n",
        "...            ...       ...       ...\n",
-       "agg_9533  2.510605  2.760446  1.965776\n",
-       "agg_9535  1.246022  1.192524  0.407104\n",
-       "agg_9536  2.809229  3.369957  2.927355\n",
-       "agg_9537  1.580334  1.533467  0.879179\n",
-       "agg_9538  2.014224  2.078125  1.374325\n",
+       "agg_9533  2.510271  2.760181  1.965718\n",
+       "agg_9535  1.245795  1.192324  0.407210\n",
+       "agg_9536  2.808904  3.369778  2.927307\n",
+       "agg_9537  1.580065  1.533221  0.879286\n",
+       "agg_9538  2.013909  2.077899  1.374621\n",
        "\n",
        "[8856 rows x 3 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1851,184 +1943,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "3a0dba6e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:16.987569Z",
-     "iopub.status.busy": "2025-11-21T06:26:16.987310Z",
-     "iopub.status.idle": "2025-11-21T06:26:50.087684Z",
-     "shell.execute_reply": "2025-11-21T06:26:50.087076Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:01.3 (539.8MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep1:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:01.4 (529.6MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep2:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:00.6 (1166.2MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep3:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:00.6 (1242.4MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:05.1 (613.2MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)\n",
-      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n"
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-      "TPU available: False, using: 0 TPU cores\n"
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-     "name": "stderr",
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-     "text": [
-      "SLURM auto-requeueing enabled. Setting signal handlers.\n"
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      "data": {
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-       "version_minor": 0
-      },
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Predicting <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> 3/3 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">0:00:01 • 0:00:00</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; text-decoration: underline\">2.24it/s</span>  \n",
+       "</pre>\n"
+      ],
       "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+       "Predicting \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3/3 \u001b[2m0:00:01 • 0:00:00\u001b[0m \u001b[2;4m2.24it/s\u001b[0m  \n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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+      "text/plain": []
+     },
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+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n",
+      "/home/lala8/miniforge3/envs/decima/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n",
       "No ground truth expression matrix found in the metadata. Skipping evaluation.\n"
      ]
     }
@@ -2062,132 +2008,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "d147aaff",
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-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:26:50.089306Z",
-     "iopub.status.busy": "2025-11-21T06:26:50.089030Z",
-     "iopub.status.idle": "2025-11-21T06:27:05.812663Z",
-     "shell.execute_reply": "2025-11-21T06:27:05.812096Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:01.2 (580.7MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:02.7 (1164.2MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n"
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-      "TPU available: False, using: 0 TPU cores\n"
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-     "name": "stderr",
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-      "SLURM auto-requeueing enabled. Setting signal handlers.\n"
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-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:433: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n"
-     ]
-    },
     {
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-       "version_minor": 0
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+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Predicting <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> 1/1 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">0:00:00 • 0:00:00</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; text-decoration: underline\">0.00it/s</span>  \n",
+       "</pre>\n"
+      ],
       "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+       "Predicting \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1/1 \u001b[2m0:00:00 • 0:00:00\u001b[0m \u001b[2;4m0.00it/s\u001b[0m  \n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'expression': array([[2.9330175, 2.8167777, 2.7431197, ..., 2.8092291, 1.580334 ,\n",
-      "        2.0142236],\n",
-      "       [2.0205972, 1.9390993, 2.3948462, ..., 1.5032245, 0.51865  ,\n",
-      "        0.8084921],\n",
-      "       [4.795624 , 4.9177237, 4.8060527, ..., 4.6602893, 2.7781048,\n",
-      "        3.1485069]], dtype=float32),\n",
+      "{'expression': array([[2.932824  , 2.8166761 , 2.7429636 , ..., 2.8089042 , 1.5800648 ,\n",
+      "        2.0139086 ],\n",
+      "       [2.0204957 , 1.9390392 , 2.3947737 , ..., 1.5031691 , 0.5186505 ,\n",
+      "        0.80847657],\n",
+      "       [4.795655  , 4.9177384 , 4.8059716 , ..., 4.6602583 , 2.7780144 ,\n",
+      "        3.1483455 ]], dtype=float32),\n",
       " 'warnings': Counter({'unknown': tensor(0),\n",
       "                      'allele_mismatch_with_reference_genome': tensor(0)})}\n"
      ]
@@ -2196,7 +2053,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n"
+      "/home/lala8/miniforge3/envs/decima/lib/python3.11/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric WarningCounter was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n"
      ]
     }
    ],
@@ -2242,106 +2099,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "2dfbdb1a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:27:05.814176Z",
-     "iopub.status.busy": "2025-11-21T06:27:05.813919Z",
-     "iopub.status.idle": "2025-11-21T06:27:06.136436Z",
-     "shell.execute_reply": "2025-11-21T06:27:06.135810Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cat: ../tests/data/seqs.fasta: No such file or directory\r\n"
+      ">CD68|gene_mask_start=163840|gene_mask_end=166460\n",
+      "CTCTGCAGAGAGCGAGGACGGTGTGTCTGCCAGCGCCTTTGACTTCACTGTCTCCAACTTTGTGGACAACCTGTATGGCTACCCGGAAGGCAAGGATGTGCTTCGGGAGACCATCAAGTTTATGTACACAGACTGGGCCGACCGGGACAATGGCGAAATGCGCCGCAAAACCCTGCTGGCGCTCTTTACTGACCACCAAT\n",
+      ">SPI1|gene_mask_start=163840|gene_mask_end=187556\n",
+      "TGCCACTTTTAGATATGTTCATGGGTGCAGATACGGCTTTATTTATTTGAGACAGAGTTTCACTCTTGTTGCCCAGGGTGGAGTGCAGTGGTGCGATCTCAGCTCACTGCAGCCTTCGCCTCCCGGGTTGAAGCGATTCTTCTGCCTCAACCTCGAGTAGCTGGGATTATAGGCACCTGCCAGCATGCCTGGCTAATTTT\n"
      ]
     }
    ],
    "source": [
-    "! cat ../tests/data/seqs.fasta | cut -c 1-200"
+    "! cat ../../tests/data/seqs.fasta | cut -c 1-200"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "0d15ea9e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-21T06:27:06.137832Z",
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-     "shell.execute_reply": "2025-11-21T06:27:07.256619Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "TPU available: False, using: 0 TPU cores\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "HPU available: False, using: 0 HPUs\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "SLURM auto-requeueing enabled. Setting signal handlers.\n"
-     ]
-    },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0c2854b5b1145a28d069646a54109b5",
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-       "version_minor": 0
-      },
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Predicting <span style=\"color: #6206e0; text-decoration-color: #6206e0\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> 1/1 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">0:00:00 • 0:00:00</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; text-decoration: underline\">0.00it/s</span>  \n",
+       "</pre>\n"
+      ],
       "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+       "Predicting \u001b[38;2;98;6;224m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1/1 \u001b[2m0:00:00 • 0:00:00\u001b[0m \u001b[2;4m0.00it/s\u001b[0m  \n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "array([[0.22026505, 0.21946757, 0.23086782, ..., 1.0358492 , 1.2124686 ,\n",
-      "        1.441437  ],\n",
-      "       [0.26515013, 0.13341773, 0.14887223, ..., 0.44430685, 0.26546437,\n",
-      "        0.3042384 ]], dtype=float32)\n"
+      "array([[0.22018507, 0.2193921 , 0.23078808, ..., 1.0356065 , 1.2123175 ,\n",
+      "        1.4411153 ],\n",
+      "       [0.2651469 , 0.1334098 , 0.14885235, ..., 0.44431016, 0.26546577,\n",
+      "        0.304252  ]], dtype=float32)\n"
      ]
     }
    ],
@@ -2366,9 +2179,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "decima2",
+   "display_name": "Python (decima)",
    "language": "python",
-   "name": "python3"
+   "name": "decima"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2380,7 +2193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.15"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
@@ -2924,13 +2737,13 @@
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        "description_allow_html": false,
        "layout": "IPY_MODEL_15a6d137d2b646a696f272e5052ef607",
-       "max": 1.0,
-       "min": 0.0,
+       "max": 1,
+       "min": 0,
        "orientation": "horizontal",
        "style": "IPY_MODEL_aa835afeee484927b37b60145e71a78d",
        "tabbable": null,
        "tooltip": null,
-       "value": 1.0
+       "value": 1
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@@ -3003,13 +2816,13 @@
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        "description_allow_html": false,
        "layout": "IPY_MODEL_90b1a10420424e418ff34bf3dd4412cf",
-       "max": 1.0,
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+       "max": 1,
+       "min": 0,
        "orientation": "horizontal",
        "style": "IPY_MODEL_f8201ba403ce4b2595d880cc13f891ad",
        "tabbable": null,
        "tooltip": null,
-       "value": 1.0
+       "value": 1
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      "8a3f3c76906944af8e4653d4cdc8e22e": {
@@ -3105,13 +2918,13 @@
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        "description_allow_html": false,
        "layout": "IPY_MODEL_ccfa2a1578f54a16b315cf45b553478c",
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+       "min": 0,
        "orientation": "horizontal",
        "style": "IPY_MODEL_45f4d8cd02c74b73a35d15e31fc33078",
        "tabbable": null,
        "tooltip": null,
-       "value": 1.0
+       "value": 1
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      },
      "90b1a10420424e418ff34bf3dd4412cf": {
diff --git a/docs/tutorials/6-variant-effect-attribution.ipynb b/docs/tutorials/6-variant-effect-attribution.ipynb
index 1f0afcf..32d5ec6 100644
--- a/docs/tutorials/6-variant-effect-attribution.ipynb
+++ b/docs/tutorials/6-variant-effect-attribution.ipynb
@@ -36,5023 +36,105 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "7d51a63c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:03:56.214581Z",
-     "iopub.status.busy": "2025-12-20T01:03:56.214457Z",
-     "iopub.status.idle": "2025-12-20T01:05:48.511166Z",
-     "shell.execute_reply": "2025-12-20T01:05:48.510364Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Usage: decima vep-attribution [OPTIONS]\r\n",
-      "\r\n",
-      "  Predict variant effect and save to parquet\r\n",
-      "\r\n",
-      "  Examples:\r\n",
-      "\r\n",
-      "      >>> decima vep-attribution -v \"data/sample.vcf\" -o \"vep_results\"\r\n",
-      "\r\n",
-      "Options:\r\n",
-      "  -v, --variants PATH             Path to the variant file .vcf file. VCF file\r\n",
-      "                                  need to be normalized. Try normalizing th\r\n",
-      "                                  vcf file incase of an error. `bcftools norm\r\n",
-      "                                  -f ref.fasta input.vcf.gz -o output.vcf.gz`\r\n",
-      "  -o, --output_prefix PATH        Path to the output prefix.\r\n",
-      "  --tasks TEXT                    Tasks to predict. If not provided, all tasks\r\n",
-      "                                  will be predicted.\r\n",
-      "  --off-tasks TEXT                Tasks to contrast against. If not provided,\r\n",
-      "                                  no contrast will be performed.\r\n",
-      "  --model TEXT                    Model to use for attribution analysis.\r\n",
-      "                                  Available options: ['v1_rep0', 'v1_rep1',\r\n",
-      "                                  'v1_rep2', 'v1_rep3', 'ensemble', 'rep0',\r\n",
-      "                                  'rep1', 'rep2', 'rep3', 0, 1, 2, 3, '0',\r\n",
-      "                                  '1', '2', '3'].\r\n",
-      "  --metadata TEXT                 Path to the metadata anndata file or name of\r\n",
-      "                                  the model. If not provided, the compabilite\r\n",
-      "                                  metadata for the model will be used.\r\n",
-      "                                  Default: ensemble.\r\n",
-      "  --method TEXT                   Method to use for attribution analysis.\r\n",
-      "                                  Available options: 'inputxgradient',\r\n",
-      "                                  'saliency', 'integratedgradients'.\r\n",
-      "  --transform [specificity|aggregate]\r\n",
-      "                                  Transform to use for attribution analysis.\r\n",
-      "                                  Available options: 'specificity',\r\n",
-      "                                  'aggregate'.\r\n",
-      "  --device TEXT                   Device to use. Default: None which\r\n",
-      "                                  automatically selects the best device.\r\n",
-      "  --batch-size INTEGER            Batch size for the loader. Default: 1\r\n",
-      "  --num-workers INTEGER           Number of workers for the loader. Default: 4\r\n",
-      "  --distance-type TEXT            Type of distance. Default: tss.\r\n",
-      "  --min-distance FLOAT            Minimum distance from the end of the gene.\r\n",
-      "                                  Default: 0.\r\n",
-      "  --max-distance FLOAT            Maximum distance from the TSS. Default:\r\n",
-      "                                  524288.\r\n",
-      "  --gene-col TEXT                 Column name for gene names. Default: None.\r\n",
-      "  --genome TEXT                   Genome build. Default: hg38.\r\n",
-      "  --help                          Show this message and exit.\r\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
-   "source": [
-    "! decima vep-attribution --help"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e6aa7ea",
-   "metadata": {},
-   "source": [
-    "Following command will calculate input x gradients of genetics variatns in the sample.vcf file for aggreation of all tasks avaliable in the decima metadata."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "963b41a8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:05:48.512916Z",
-     "iopub.status.busy": "2025-12-20T01:05:48.512767Z",
-     "iopub.status.idle": "2025-12-20T01:13:14.876515Z",
-     "shell.execute_reply": "2025-12-20T01:13:14.875597Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "decima - INFO - Using device: 0 and genome: hg38\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:07.1 (439.7MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:08.4 (86.1MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:07.8 (399.0MB/s)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
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-      "decima - WARNING - Warnings:\r\n",
-      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38.If this is not expected, please check if you are using the correct genome version.\r\n"
-     ]
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-     ]
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-     "output_type": "stream",
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-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\r\n"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:01.9 (1603.5MB/s)\r\n"
-     ]
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-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep1:latest', 720.03MB. 1 files...\r\n"
-     ]
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-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
-      "Done. 00:00:02.0 (356.3MB/s)\r\n"
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-     ]
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-      "decima - WARNING - Warnings:\r\n",
-      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38.If this is not expected, please check if you are using the correct genome version.\r\n"
-     ]
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-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \r\n",
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-     ]
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-      "decima - WARNING - Warnings:\r\n",
-      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38.If this is not expected, please check if you are using the correct genome version.\r\n"
-     ]
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-     ]
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-     ]
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-     ]
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-     ]
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-     ]
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-     ]
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-     ]
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-     ]
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-     ]
-    },
-    {
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-     "text": [
-      "\r",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Writing attributions...:  96%|█████████████████▎| 46/48 [00:20<00:00,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Writing attributions...:  98%|█████████████████▋| 47/48 [00:20<00:00,  2.24it/s]"
-     ]
-    },
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Writing attributions...: 100%|██████████████████| 48/48 [00:21<00:00,  2.24it/s]\r",
-      "Writing attributions...: 100%|██████████████████| 48/48 [00:21<00:00,  2.24it/s]\r\n",
-      "decima - WARNING - Warnings:\r\n",
-      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38.If this is not expected, please check if you are using the correct genome version.\r\n"
+      "Usage: decima vep-attribution [OPTIONS]\n",
+      "\n",
+      "  Predict variant effect and save to parquet\n",
+      "\n",
+      "  Examples:\n",
+      "\n",
+      "      >>> decima vep-attribution -v \"data/sample.vcf\" -o \"vep_results\"\n",
+      "\n",
+      "Options:\n",
+      "  -v, --variants PATH             Path to the variant .vcf file. VCF file\n",
+      "                                  needs to be normalized. Try normalizing the\n",
+      "                                  vcf file in case of an error. `bcftools norm\n",
+      "                                  -f ref.fasta input.vcf.gz -o output.vcf.gz`\n",
+      "  -o, --output_prefix PATH        Path to the output prefix.\n",
+      "  --tasks TEXT                    Tasks to predict. If not provided, all tasks\n",
+      "                                  will be predicted.\n",
+      "  --off-tasks TEXT                Tasks to contrast against. If not provided,\n",
+      "                                  no contrast will be performed.\n",
+      "  --model TEXT                    Model to use for attribution analysis.\n",
+      "                                  Available options: ['v1_rep0', 'v1_rep1',\n",
+      "                                  'v1_rep2', 'v1_rep3', 'ensemble', 'rep0',\n",
+      "                                  'rep1', 'rep2', 'rep3', 0, 1, 2, 3, '0',\n",
+      "                                  '1', '2', '3'].\n",
+      "  --metadata TEXT                 Path to the metadata anndata file or name of\n",
+      "                                  the model. If not provided, the compabilite\n",
+      "                                  metadata for the model will be used.\n",
+      "                                  Default: ensemble.\n",
+      "  --method TEXT                   Method to use for attribution analysis.\n",
+      "                                  Available options: 'inputxgradient',\n",
+      "                                  'saliency', 'integratedgradients'.\n",
+      "  --transform [specificity|aggregate]\n",
+      "                                  Transform to use for attribution analysis.\n",
+      "                                  Available options: 'specificity',\n",
+      "                                  'aggregate'.\n",
+      "  --device TEXT                   Device to use. Default: None which\n",
+      "                                  automatically selects the best device.\n",
+      "  --batch-size INTEGER            Batch size for the loader. Default: 1\n",
+      "  --num-workers INTEGER           Number of workers for the loader. Default: 4\n",
+      "  --distance-type TEXT            Type of distance. Default: tss.\n",
+      "  --min-distance FLOAT            Minimum distance from the end of the gene.\n",
+      "                                  Default: 0.\n",
+      "  --max-distance FLOAT            Maximum distance from the TSS. Default:\n",
+      "                                  524288.\n",
+      "  --gene-col TEXT                 Column name for gene names. Default: None.\n",
+      "  --genome TEXT                   Genome build. Default: hg38.\n",
+      "  --help                          Show this message and exit.\n",
+      "\u001b[0m"
      ]
-    },
+    }
+   ],
+   "source": [
+    "! decima vep-attribution --help"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e6aa7ea",
+   "metadata": {},
+   "source": [
+    "Following command will calculate input x gradients of genetics variatns in the sample.vcf file for aggreation of all tasks avaliable in the decima metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "963b41a8",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "Computing attributions...: 100%|████████████████| 96/96 [00:53<00:00,  1.80it/s]\n",
+      "Writing attributions...: 100%|██████████████████| 48/48 [00:24<00:00,  1.97it/s]\n",
+      "decima - WARNING - Warnings:\n",
+      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38. If this is not expected, please check if you are using the correct genome version.\n",
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "Computing attributions...: 100%|████████████████| 96/96 [00:52<00:00,  1.84it/s]\n",
+      "Writing attributions...: 100%|██████████████████| 48/48 [00:25<00:00,  1.92it/s]\n",
+      "decima - WARNING - Warnings:\n",
+      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38. If this is not expected, please check if you are using the correct genome version.\n",
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "Computing attributions...: 100%|████████████████| 96/96 [00:52<00:00,  1.83it/s]\n",
+      "Writing attributions...: 100%|██████████████████| 48/48 [00:24<00:00,  1.93it/s]\n",
+      "decima - WARNING - Warnings:\n",
+      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38. If this is not expected, please check if you are using the correct genome version.\n",
+      "decima - INFO - Using device: 0 and genome: hg38\n",
+      "Computing attributions...: 100%|████████████████| 96/96 [00:52<00:00,  1.82it/s]\n",
+      "Writing attributions...: 100%|██████████████████| 48/48 [00:24<00:00,  1.94it/s]\n",
+      "decima - WARNING - Warnings:\n",
+      "decima - WARNING - allele_mismatch_with_reference_genome: 10 alleles out of 48 predictions mismatched with the genome file hg38. If this is not expected, please check if you are using the correct genome version.\n",
       "\u001b[0m"
      ]
     }
@@ -5065,28 +147,20 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "a477e150",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:13:14.878723Z",
-     "iopub.status.busy": "2025-12-20T01:13:14.878545Z",
-     "iopub.status.idle": "2025-12-20T01:13:15.011291Z",
-     "shell.execute_reply": "2025-12-20T01:13:15.010655Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "example/vep_vcf_attributions.h5\r\n",
-      "example/vep_vcf_attributions.h5_0.h5\r\n",
-      "example/vep_vcf_attributions.h5_0.warnings.log\r\n",
-      "example/vep_vcf_attributions.h5_1.h5\r\n",
-      "example/vep_vcf_attributions.h5_1.warnings.log\r\n",
-      "example/vep_vcf_attributions.h5_2.h5\r\n",
-      "example/vep_vcf_attributions.h5_2.warnings.log\r\n",
-      "example/vep_vcf_attributions.h5_3.h5\r\n",
-      "example/vep_vcf_attributions.h5_3.warnings.log\r\n"
+      "example/vep_vcf_attributions.h5_0.h5\n",
+      "example/vep_vcf_attributions.h5_0.warnings.log\n",
+      "example/vep_vcf_attributions.h5_1.h5\n",
+      "example/vep_vcf_attributions.h5_1.warnings.log\n",
+      "example/vep_vcf_attributions.h5_2.h5\n",
+      "example/vep_vcf_attributions.h5_2.warnings.log\n",
+      "example/vep_vcf_attributions.h5_3.h5\n",
+      "example/vep_vcf_attributions.h5_3.warnings.log\n"
      ]
     }
    ],
@@ -5121,21 +195,14 @@
    "id": "1a995343",
    "metadata": {},
    "source": [
-    "Same as CLI API, `variant_effect_attribution` python api provides equlivant way to caulcate attribution for vairaints."
+    "Same as CLI API, `variant_effect_attribution` python api provides an equivalent way to calculate attributions for vairaints."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "9309f1d5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:13:15.013817Z",
-     "iopub.status.busy": "2025-12-20T01:13:15.013521Z",
-     "iopub.status.idle": "2025-12-20T01:13:35.782497Z",
-     "shell.execute_reply": "2025-12-20T01:13:35.781899Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import h5py\n",
@@ -5156,178 +223,32 @@
     "`chr9_133251979_C_T` is eqtl variant for `CD4-positive, alpha-beta T cell`. [https://platform.opentargets.org/variant/9_133251979_C_T](https://platform.opentargets.org/variant/9_133251979_C_T) for `ENSG00000175164` (ABO) gene."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "43857d8a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:13:35.784960Z",
-     "iopub.status.busy": "2025-12-20T01:13:35.784556Z",
-     "iopub.status.idle": "2025-12-20T01:13:35.788035Z",
-     "shell.execute_reply": "2025-12-20T01:13:35.787463Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_variants = pd.DataFrame(\n",
-    "    [\n",
-    "        {\"chrom\": \"chr9\", \"pos\": 133251979, \"ref\": \"C\", \"alt\": \"T\", \"gene\": \"ABO\"},\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7d9ad6a4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:13:35.789267Z",
-     "iopub.status.busy": "2025-12-20T01:13:35.789135Z",
-     "iopub.status.idle": "2025-12-20T01:14:09.603776Z",
-     "shell.execute_reply": "2025-12-20T01:14:09.603108Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmhcelik\u001b[0m (\u001b[33mmhcw\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:02.3 (1351.0MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'rep0:latest', 720.03MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:04.2 (171.5MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:01.9 (1636.2MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing attributions...:   0%|          | 0/2 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing attributions...:  50%|█████     | 1/2 [00:01<00:01,  1.55s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing attributions...: 100%|██████████| 2/2 [00:02<00:00,  1.09it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing attributions...: 100%|██████████| 2/2 [00:02<00:00,  1.03s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Writing attributions...:   0%|          | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Writing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.77it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Writing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.77it/s]"
-     ]
-    },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "43857d8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_variants = pd.DataFrame(\n",
+    "    [\n",
+    "        {\"chrom\": \"chr9\", \"pos\": 133251979, \"ref\": \"C\", \"alt\": \"T\", \"gene\": \"ABO\"},\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7d9ad6a4",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "Computing attributions...: 100%|██████████| 2/2 [00:02<00:00,  1.36s/it]\n",
+      "Writing attributions...: 100%|██████████| 1/1 [00:00<00:00,  1.92it/s]\n"
      ]
     },
     {
@@ -5366,14 +287,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "4126fb8d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:09.605269Z",
-     "iopub.status.busy": "2025-12-20T01:14:09.605126Z",
-     "iopub.status.idle": "2025-12-20T01:14:09.608393Z",
-     "shell.execute_reply": "2025-12-20T01:14:09.608006Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5400,14 +314,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "3022232e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:09.609713Z",
-     "iopub.status.busy": "2025-12-20T01:14:09.609549Z",
-     "iopub.status.idle": "2025-12-20T01:14:09.653051Z",
-     "shell.execute_reply": "2025-12-20T01:14:09.652647Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5491,75 +398,14 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "2cb59750",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:09.654155Z",
-     "iopub.status.busy": "2025-12-20T01:14:09.654030Z",
-     "iopub.status.idle": "2025-12-20T01:14:09.872102Z",
-     "shell.execute_reply": "2025-12-20T01:14:09.871661Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Loading attributions and sequences...:   0%|          | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00,  9.58it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00,  9.52it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Loading attributions and sequences...:   0%|          | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00,  9.64it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00,  9.59it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00, 14.85it/s]\n",
+      "Loading attributions and sequences...: 100%|██████████| 1/1 [00:00<00:00, 17.29it/s]\n"
      ]
     },
     {
@@ -5592,34 +438,13 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "e8b3b4ed",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:09.873470Z",
-     "iopub.status.busy": "2025-12-20T01:14:09.873336Z",
-     "iopub.status.idle": "2025-12-20T01:14:21.088192Z",
-     "shell.execute_reply": "2025-12-20T01:14:21.087475Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:04.6 (675.4MB/s)\n"
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
      ]
     }
    ],
@@ -5632,14 +457,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "5d1ae344",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:21.090109Z",
-     "iopub.status.busy": "2025-12-20T01:14:21.089956Z",
-     "iopub.status.idle": "2025-12-20T01:14:21.093206Z",
-     "shell.execute_reply": "2025-12-20T01:14:21.092802Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5660,14 +478,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "7d8e839e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:21.094280Z",
-     "iopub.status.busy": "2025-12-20T01:14:21.094159Z",
-     "iopub.status.idle": "2025-12-20T01:14:21.096467Z",
-     "shell.execute_reply": "2025-12-20T01:14:21.096113Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5688,19 +499,12 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "b983e884",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:21.097637Z",
-     "iopub.status.busy": "2025-12-20T01:14:21.097512Z",
-     "iopub.status.idle": "2025-12-20T01:14:22.661203Z",
-     "shell.execute_reply": "2025-12-20T01:14:22.660820Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<logomaker.src.Logo.Logo at 0x14bcf51e4c10>"
+       "<logomaker.src.Logo.Logo at 0x7f1df64ee710>"
       ]
      },
      "execution_count": 13,
@@ -5709,7 +513,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x200 with 1 Axes>"
       ]
@@ -5726,19 +530,12 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "1684816e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:22.662542Z",
-     "iopub.status.busy": "2025-12-20T01:14:22.662415Z",
-     "iopub.status.idle": "2025-12-20T01:14:23.471789Z",
-     "shell.execute_reply": "2025-12-20T01:14:23.471367Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<logomaker.src.Logo.Logo at 0x14bcf51ba810>"
+       "<logomaker.src.Logo.Logo at 0x7f1e19f62e10>"
       ]
      },
      "execution_count": 14,
@@ -5747,7 +544,7 @@
     },
     {
      "data": {
-      "image/png": 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MRZJ1zUNqdLR+Oq5sumYe6BQWll2uZWVj0zUioprhUKBTUFCAnTt3Yvjw4foG3NwwfPhwbN68ucoKlZ+fj8zMTItHfn5+lW2fiIhqH6MRePttYOhQ4Jdf7F9PC2p8PXxLanS0eVqNjrt7lRYVgGWgk5NTdjlrdIiIapZDgU56ejqMRiNCQkIs5oeEhCAlJaXKChUfH49GjRpZPOLj46ts+0REVPv897/As88Ca9cC99wDrF9v33paMzVtHB1A0k0DlmPdVDXzQCc7u+xyJiMgIqpZLqjMr7wXX3wRcXFxFvPY7I2IqP7KzgamTdP/NpmAqVOBXbtsBysWNToeljU6rgw2bNXoaM3lWKNDRFQzHAp0goKC4O7ujtTUVIv5qampVZpowNvbm4ENEdE15MsvgVKnFuzZAyQnAxERFa+rBTXmNTqlAx1rfWgqy1aNjq/EXAx0iIhqiENN17y8vNCrVy8kJCSUzDOZTEhISED//v2rvHBERHRt+O0359etKNDR+ua4okbH/H6ctRod80BH6ytERETVx+Gma3FxcZgwYQJ69+6Nvn374v3330dOTg4mTZoEABg/fjzCw8NL+tQUFBTgwIEDJdPJycnYs2cP/P390aZNmyr8KEREVBfl5gLr1jm/vtYfx9fTt0x6aVc2XfPwkEdRUcU1OoAEQg0bVn0ZiIiofA4HOmPHjsWFCxcwbdo0pKSkoHv37li2bFlJgoKkpCS4mQ0Xfe7cOfTo0aPk77fffhtvv/02Bg8ejLVr11b+ExARUZ22dy9QUOD8+vY0XXNVQgAfHwlyrNXo+Pnp01lZDHSIiKqbU8kIpkyZgilTplhdVjp4iYqKglLKmbchIqJrwJ49+rSHBzBlCrB4MXDihH3rV5SMwJVN1wA90LFVo5OV5Zr3JyKi8jk8YCgREVFV2rdPn548GXjvPWDNGqBBA/vWr+kaHYCBDhFRbcRAx4qcnMo1oyAiIvslJ8uzmxvw8ssy3bIl8Pe/27e++Tg6JX10ivvtaDU65WZdMxjKf9hBC3SsNV0zz8qWmWnX5oiIqAox0Cll5kygaVOgSRNg7tyaLg0RUf2yfz/w5pvAn3/q89LS5LlnT6BZM33+ww/bF2+UNF3z9C1To6P1k3F1jU7pGhuTyfKGGWt0iIiqX60cMLSmLFkCvPiiTOfnAw89JHcVhw+v2XIREdUHK1cCI0YAWrfNDz4AnnpKD3S6dbN8fevW+msrYt50rXQfHa35W35+pYtvlRbopKdbzj9/3vJvBjpERNWPNTrFCguBxx4rO3/OnOovCxFRfXPhAnD//ZaBS1wccOiQHuhERZVdz54anZL00h56jY7WnM3fX17jqqZjWqBTerDTEycs++iw6RoRUfVjoFNs6VLg3LmaLgURUf302Wd6QKMxGoHDh/X+LdYCHXtY1Oh4Wq/RuXzZuW3bogU6KSmW848ft+yjU7pGRylU3D/Izj5CRERUPgY6xX75paZLQERUPykFfPml9WVXrujTTgc6ReVnXdMCHfP3qUpaMJOZCVy9qs8/dgzw9NSzvmVkWK5XOjAiIqKqx0CnmPnwP8OHS18d82YHRETknBMnLMfEMa/pMA9AzBMROMI8GUF5fXTKrdFRSn+UnmcH889y/Lg+vXevVMpo55HSfXYOHbJr80REVAlMRgBpTnH6tEy3aQMsXCjtugcOBP73v5otGxFRXbdunT7dpQuwZYsEBTfeaFkLYh40OELro/P6+tfRwFMim9J9dDIyJBOaWxXf3jMv819/yecDgN275dnXV5rmlW4afegQMLRqi0JERKUw0IEe5ADAP/6hnxhHjSrb3MCW9euBFSuAdu2AceOk6QIR0bXs8GF9eu5cqWXp1g349FPgwAF9mbOBjlZ7M2fXnDLztBodpaSfTKNGzr1HecwHNU1MBMaOlZtn2thAWo2O9reGNTpERK7HQAfAmTP69JAhlsvGjrV/O3PmAI8/LncNAWDePElZzSZwRHQtS0qS56gooHdvff4dd1jeTHL2t1ILaqzNMw9ELl+2DHSUqnyf/8aN9en9++V59Wp9nvaZTp6U5AvaAKa7d8OyeZxWEDubzBERkW3sowP9JOzpCbRta7lMOynZsmQJ8OijepADyMlu6dKqKSMRUV2l/cZ2724ZWBgMQECA/rfTTdeKm6lZzCtuzmYe6Fy8aPma0v1mnNGkiT69ciVw6RLw/vv6PG3A0txc4MgRmc7MlNofIiJyLQY60Gt02rVzvqnZ9OnW5/PmHBFd67TfWK3/ijnzm0MeNtoYHDoE3H47EBsLzJih9++pqEZHa4oMWDZTBvR+NJVhXqOTmwt07Qps3arPa9hQn961S563brX83ETXGqXk+3j0KK+TyLUY6EA/CUdGOrf+wYP6CQwAbr0V6NOn8uUiIqrrior0/int25ddbh7cFBSUv50//pDf1cWLgW3bgFdflX6UgPVAJ9+YD6PJaFGjc/Kk5WuqOtAByiYdCArSp7UAiMMZ0LUsK0v6MEdFyQ3mfv0sszISVSUGOtBHrDY/ITpi+XJ9eto0ORFv3SqjfhMRXcsuXtRrL6wlAjAPdMwzsJm7fBkYPx7Izracv3OnPFsLdADgatFVi991rekYIOPYlK7hcYZ50zVrzAOdr74Cfv4Z+OKLyr8vUV1UUAAMGgQsWKDP27YNeOKJmiuTo4xGYN8++f0xGmu6NGQLAx3odxGdDXS07DmNGwMvvSTTBgPw1lvSjIGI6FqVn69Pmzcj05g3F87Ls76NL7/U+9e4uQEtWujLlFIl/XFKyynIsfhd37RJn1650kbB7VS6Rqc080AnKwu4+26p5SK6Fn3+ObBnT9n5hYXVXhSnnDwJDB0KxMRIYpV27YDNm2u6VFQRBjrQAx2t06ijtLuEAwcC3t76fA8P+RIQEV2rzJujWbuZ5OWlT5cX6Gh3f729JfFLUhKwYwcQFgYUmgphVNZvq+YW5lrUuCQm6gHTH3848CEq4EigQ3St+/RTfXr8eGD2bKBXr5orjyNSU4G+fYE//9TnnTgBvPJKzZWJbGOgA73q0VZH2N+P/I4hXw3Brd/fajFfGyOif38XFI6IqA4zv1NrLata6c78pWnNRADgoYeAkSNlulcvCYDKa7YG6IGOea3RggUysGdV9ZOxFcgw0JGarD//lCbddeXO/bXg/Hlper9jR/U0wUpNle8eAEycKE05H38c2LBBaklqu1dfBdLT9b/tzcpLNYuBDvQ7itZOsubWnV6HdafXYcmRJbiQcwGA3IHUOp9GRbmujFR/ZWXJj7/5D2htYTTKyVDrx0bkKFtN04KD9Wlr6Z5PnNDX05IPaLp1Q7nN1gBJO20wAKGh+rxnngEGD6448YEjGjSwTJFd2rUc6CglzQ7btpV+Gf36AdHRQEJCTZfs2pafL/2Jo6OBm2+WJB/duulBSIUMhvIfNmjjTAHA5Mn6Kj4+wMsvO/dZqkt+PvC//8l0eLj0z8nLkwGQnU2LT9WDgQ70QCen/PMlACAxTR/44K8L8otgHhxVdLIjKi0nR6q8Q0Ml7W6zZnIhVxVje1SWySQ/6q1bS/OgwEDgppskFSiRI8ybpln7jTUPBEpnRQMsL74GDiy73FaNDgA0b67Py8srO55OZYWHl7/MPJC71nzxBfDgg3InX3P2LPDjjzVXJgKefhp47TXLGw8HDgDr1rn2fbWa2cBAoEcPy2VutfxqNDFR//16912gZ09pBTRpEvDBBzVbNqpYLT+0qocWjduq0TEPdLRp8yxBDHTIXkoB99wDvP665XH3xx/A9u01Vy7N9OnAhAl6ViqlpPM20+KSo8z7LVoLdLy89OZrp06VXa4dgw0aWM/apgUz7Zu2x+O9HsfjvR5Hz+Y9LZaZBzquEBZW/rKWLV373rVVVhbwwgsy7e0t0++8I4PGUs1JTNT7yQQHy/9k5szqaZGiJW7q2LHuNfvShhDx8gJuucVyWevW1V8esp+NXinXBu0kVVHToYy8DJzJPIPY8FhsTd5aEujYUVtLVMaiRcDSpTLdty/wz39KCt3acGfoyBEgPl6mIyLkAsXNTaroiRzVtKkcPyYTcOWK9dcEB8vxX7pGRym92WR5nf61YGZo1FDMHj0bAPDG+jew6/yukmXmTddcoaIanWbNAF/f8lNn11fr1gGXLsn0jz/K+HIA8NRTrq85oPItWiTPnp6ShVC7SH/iCRkT0CZtdE/t4seB0T5tfZerQmEh8MMPkt0tKQkICQHuuguYMkW+h87SMsV162Y9eyTVXgx0oKcqPXhQvrPWghetqdpNrW/C/rT9JYGOedtM9mOonZSS/+2FC3LB065dzQeoWs1IVJSc9LXj6IEHgLS0GisWAGDhQumb4+UFbNyo35F+5BE98QaRvTw85GbS2bPlHz/BwbJs2zbL+X/+CWRkyHR57eBzCqWayM9TT5upTWv9d6KjnS+/PSoKdAwG+Q5da98dLTNVZCQwerQ+38MDGDasZspE0vEfkL455jURAQFAbKxr31sL9l3V+qWgQGpbzPuAnTolSTD69QOuv975bV+Qbtlo1qxSRaQawKZr0C/kLl2ybEtsTgtsOjXrhA5BHZCYlgillEW6VO2EXG+ZigCjEz14U1YDm/4PWNoDWDkQ2Pk0kHnE5moWrp4HriQCeRccWu2bb4BOnYDOnYEhQ4AOHWR09vL+z9VBKWDNGpmeONHyAs7XVy4MqoTJCKSsAo5+Apz4Criy3667b1rZbr3VstmNh4fsx5pmMskd4tGj5QKzRQvgttuAtWur8E0Ks4C0dXLs5pypwg1fm7TjqLzOzlo/lhMnLMfY+OgjvelbVpb1dbVaG19P/XatNq0tc3Wa/4oCHaAKv9N1iBa0Dh5c8zeWSKf9X4YMce375OdLgLFundSsAHp/PVfVbv7nP3qQ8/e/SzPwpUvl/FBZWpldWRtFrsEaHVgOPrdrl2X7y0OH5OJYC3Q6BnVEx6CO2HV+F5KzkhEREIGICLlbaa0jbZ2nFHD6O+DobCB9EwAF+LUAou4HYt60vf7+GUDiqzLt2Qhw85LtuPsC3eNtr39+BbB/GnBxqz4voBMwZCnQoOLG7998IzUkgAzsNWSIdEJeuFBq30JCbL+9K1y6BCQny3TpDplVJu1PYPN9QG6pi/SYeKDTCxWuunu3PPfu7aKyVYJS0nfom2/k7/BwGZl+2TJ5rvTJu+gqsPsZ4OQ8wGjWeSr8NmDQr5Xc+LWrZUtpJrN7t2WtuVKSfKNVK/21zz8vKW+XLpWA9o03ZP7ly9Zr3LVgxlqNTnUFOu3bW5+vBWnXYj8dLeGDq5sNXouKiqQp8fffS/Yvk0mOwcceAx59tPz1TCb5HgGuO/9pfbO+/dby5u+gQXo/IK0MVe377+V5zBjgww/1+SNGVD7Lol1Jq0xGIG0tcHYRkJ8GeDUBgvoDLe4CPJwckZ4qjYEOLAOdb77RA520NOD996XjXmJaIgwwoF3TdugY1BGAzIsIiEC7dhLo1MvRcQ+/Jxd+bp5A5P9JcJHxF3B2oe1AJyepOMgxAAO+B1qOkflX9gG5Z22/98VtwLpbAGUEIscBQQOA/IvAmZ/kuYJARynp6A9I+9wFC/TOj2+/bZnytrqZ35l2STV4YSbw5+1AwWUg6gEgehJg8ABSVwNutvNgak0wa2O2qFWr9CBnzhzg4Ydl+tKlKkrisOtp4PjngG9zoMPrgF+E1IRd3FIFG792ab+xp07JmB19+sjfixbJQM3mHdRXrJCbS8eOyd9as7OCArkzXLp2RGueZh7o+HpIjY7WrK11awmQHOhO4BBrNyyaNZP+SYD1mlDzJA31kb3j012zTEXyu3JlnxyYDSKBZgMBr4qrDEwm4G9/A377TfbtrbdK09D9++VCv6JAx/z4d9X/ZcwYufEUEADMmCEB2PHjwO+/6zc0tBqeqpSWJkEfIAORmjMYKv990xKhlJux0ZgPrL8dSFku51v/1kBRNnDsU8A7GAi7uXIFIKfxJwhy4vTzk+xX338vA9INHQrcd59+NyoxLRHhAeG4nHcZzRo0K5l3c5ub0b49sHq19Ge4elXv8FZQICfrTp1q6INVlqkQODBTpq9fBISZVXXl25Gf9cxP8hxxOxA5Vp/fOEYethz5jwQ5HZ4Berytz+/yipQNALKOyt2TSzvkh8anGdC4F/7KexSHD0vLzOeft8zwYj5Sek0wb6pWXnOcSjnziwQ5TfsB/ebpt8CD7Wug7OMjaUddUrZK+vpreR40SA9yAPmfjhhRyY0XZgKn/gcY3IChq4BGxV/clvfInTpymnmNx4MPAlu2yMXP449LGvPSmbjM+7OYr7tuneVFjFJmTdc89KZrpWt0vL2BNm3KpkevqiZVTZvKeUTLEAcAMTH69q0NJl3fs49pF4YXHGttfG1IWyfNua+eA7ybAd5BQM4poFFnYETFd2wSEiTIMRjkusO834mtoQnc3fVrHVf0Bd2zR4IcAFi8WJotal54Afi1uFL81Cn5rlRlk07zz+6KDHJdushzYqLUqJUJFE/MlSAnoCMwZJl+I/byXsCjerMXGI1yLdqgAZuNAnUs0Ll0SdLvJiXJdIMGchK84w758jrL01PGZ1i5Uv42P5GOGQOk5aThQq78Woe/qzfGLmnOJhU8yMiQQbj+3/+Tuy5TpwLDh9sX6Cgl2a7On5cLzMBA2W6NDjZ3aQeQfwHwDdeDnPTN+m2hxj0As4uLMjKLc0kGdpfni9uAg8UBS4NIoMesit8/pfgf0vLe4vLsBHKL23wFdJC/tzwAeDQEoicCfpFAXipw4r84W/QQtC5otS31Y1CQHLs5OdInocqlb5Ln5jfJr1zWUSD5N5nnHQy0ur/C1SMjJTtWbWyKqaUndclFYvpWwJgnx1ajTnKcn/pWXx5xB+DJdDsV2Ze6DzvP7YS3hzfGdRkHQ/FZ1rxJYWKifAfMx/Do0EGCVS1Ll7mgIHmkp0tzmAce0E/eX34J5LYr23StdB8dQDrAlw50tIuXqtCrl2Wg062bPt2jhwRb+fn6PK1Wq6rk5srdZnd3qY115I59YaGse+WKZJQKC6v8uCbdu0srB+0uOxUz5gEbxkjTpti5QKuJckAb84HLu2yuPn++PA8cWLZzvT1p1Lt3l2akrvi//PGHPIeHWwY5gByXXbvqf//8MxAXp/+9YoWM1eYs8/G6bA0V4oyekrEeV67IcW2+73NygAanv5M/Or8sQY7JCMBUfMPM9d3hDxyQrK2//w6kpMh1rZub3LRfsqRq3uPSJblmuXhRfl9atpTrq9o+BlItL57uzTelTWlcnBxUnTvLj3lCgvxTK6uiL9j+1P3W56fJ/JvNaiTffluCm27d9Fz1tvznP3IHolcvmV6xQlIjDhpkZ+FdpTBbnr0C9XkJg4FVA+VRuv9HaW7F7cOMxVc0+ZeAC+uBMz/qQUxFtPU8ii9gDr0jTbL+vB04/b00M1JGYOhyoOd7QIenpd/PiO1o1ERvm1bb7ih6eADXXSfTCxaUXV5QAOBqqgSF624FVvSTR8INwL5XbL+BMsmzobga68o+aX64+xlpimiDdlH6009y56pM2WqQVhtmV22TnaN1l1DFtYRuZm0ctjygP/IryD9PMJqMGPfzODy25DHc98t9WHpsacmy6GjLzGfmQQ4gF0E3l9Oyw2DQa0RWrACefFL6Uk6fDjzzjH19dADr2x861IEPaIN2IaQZMECf9vIq2+etKjJcpaVJp+sWLeQcMn68PDp1AubNs73+mjXSgsHfX/bPs8/K+tHRlf/d1H7jdu2SZlXmXHKDp65IWSVBTmCMNCtWRuD0AmmZkJOkn/fKoTUt1ppFOkq7QF+40LIPjclU+Ztb2vmhvAyJ0dF6i4pXXpFg5/x54OWXZSyfyoiK0m94myc0qSo9euink5de0v8PR47ITW1cLb4Q9SuuydnzT2CBlzx2x5XZXlU6dUqGqfj8c+mndfGi1OicPy+Dw1bWgQPAjTfKNfeTT0rfyfnzgYce0ltZuNqWs1sw+rvRGP3daGw+42A/EeWEjz76SEVGRipvb2/Vt29ftXXr1gpf/8MPP6j27dsrb29v1aVLF/X77787/J5ye1UpG2/ltHPnlHJ3199He4wZo9T7m99XmAHV9ZOuati8YWrYvGEqID5A+bzuo4qMRUoppWJjy64LKPXTTxW/74oV8jp3d6WOHHHNZ3NazhmlvjMotcBHqbyLMu/iTqWW9lTqOyiVcbji9U9+J69b2lMpk0nmXU2VeX/E2H7/lYPktce/lL9zzyu19xWZt/s5ef7eXani/4FaeZ1SPzVW6qfGquDk7yooSPbtK69YbtZkUqqw0N6d4BrvvKMfI6++qlR+vlIXLij1zDNK/bHoolILw5Wa76nU6QVKFV2VlfKvKJW+zfbGj8yWfZMwXP42FiqVeUT/X9jw++962R56SKnMTKVycpT68EOl3ntPKXXlL6V2xim1aqhSG8cptXmSUpsnyrTJ6PQ+sce//y3lCguTMpnTDrES2oewV8nx7qtUXrpsMOesUkt7yL7LOlnZ4tdrc3fNVZgB9cXOL1TEuxGq6yddS34flVLqtdes/0YuWybLFy60vjwtTX5HrS0LCFDqmeXPKMyA+v2Ifl7Zc36Pwgyoe3+6t2ReRoZSHh76uhERShlLH662jpkKlv/xh77YYFDq4kXL5c8+qy/39lYqO9vB97Zi4EBZ5f/+T6mCAstleXkVr3v4sJQDUOrXXy2XFRXJozLOnVPK01O236qVUosXK7V5s1KPPqrUY49Vbtt12plF8nuyrJf8XZij1O9dlZrvLfOvpla4uvY9atHC9v/YmjVr9EMtNlapdeuUWrVKqZEjlfr4Ywc2ZOV41b4D7u5KnThhfbWpU61/l4cOdfyzlHbHHbKt9u3lvKVJSlLq9OnKb3/0aL28LVpImT09lRo2TCm1eoT8/45+Ki++uFOpHVNl3o6nKv/mFZg2TcrUunXVb9tkku0CSj33nPXlrlZQVKC6fNJFtfmwjWr7YVvV5ZMuqqCowPaKxRyu0VmwYAHi4uIwffp07Nq1CzExMRgxYgTSymnwuWnTJowbNw4PPfQQdu/ejTvuuAN33HEHEhMTHXpfLZJ21R3l5s2td+Jr315vovb5rZ9j1fhVWDV+FYZHD0deUR5OXJZbU//+t/Xt2qrS0+6aeXrWXBawcvlFAKHD5Q7TlgekKZp/a+nDYI/w0dKx8vIuYMtEyQSW7kCn7lYT5HnvC9L0yuCuHwge/kDD9nI3LHmxzBv4ExA2Cii4DE+PgpL/5+uvy4Ccv/8u/QGGDq2GZllKAVnH5e7dmYVyx+7Mz8D5lUBRDh55RE9JO3263Ilq1kxGqW5k2gNcTQYatJIEDu4+wNqRwIo+wJ93AtmnKn7vlncD7n5A6ipgzwtA1hG9yZ8dbr5ZxhwAgP/+V5pR+vvLQH8ouAysHAAceR/o+zkw4Dtp0hVyA9D8ZmlzXlk5p4FzS4GT3wCnvgNOfQ+cng9kHsGECdIH7tw56ZPz22/SDGPWLH0UdqeVHO9XgY1ji5uRGPT+YFSuq4VXMW3tNAR4ByC4QTBuaHUD9qftx3f7vyt5zWOPlU1w4e6uZyS7/fayzbnuvlu+F3feKTXe1thboxMQIM19NA88ULVNLmJj9f6ZffqU7Qto3pRn2DBYDE3gDKXkTqv2fqUTrNjqfH3mjN6UrvS+dXe3c+T6olwg4wBwYYP81qWsliag+elo3lxqmwD5vb3tNqmZ+/xzO7Zbn4UOl/PipV3yO+fhB9yyDwgoJ3VfKQ89JDUmZ85IBsqjR6WVy+bNkjzJlsGDpdUJIOmftb+XLq14PXvcdJP0hTMaJWHC2rVSq7BxI/CPf8hrnn8eaNiw8u9lzdSp8nz4sGRanDIFuPdeuY4zb1bqrFdf1b8XZ85IjWihdnrQrlcS/y3fh8AYoEn1pC7VukecPy+JsaqSySTNhgHr2SOrow/Qu5vfRWJaIt644Q28ccMbSExLxLub37V/A45GVn379lWTJ08u+dtoNKqwsDAVHx9v9fVjxoxRo0aNspgXGxurHnPwls577ynl66tUYKBSTz0ld5ffektqXI4edfRTWJeTo1SnTnrEft11Sl29qlS/L/opzIC6cvVKyWtfTnhZYQbULwd+KZn3zTf6HSxAov/8/Irf02hUavx4uQPYsaNSb76p1Jdfyue9774q+FBX/lLq9A9KJS9V6tIepS7tVerSbrnbUJBle/3sU0r93lnuSpg/FvgplW12i6S8u5HnE5T6qWnZ9deOtv3eJqNSWx8tu6521yTtT6V+Dpa78AnDlNo8Qalfo2T5mYWqoECphx9Wys3N8s6Rm5tSJ0/auf+clTBMynFktuzn9G1KHflYqa0PK3VuuVJKqT17lOrSxbJs/v5K7dxhVOrPu2T9jfdJrU7yH0r91k7mZR6z/f5nFyv1Q0Mr+/0Wu4p/9qxSgwdbls3dXamff8zXP1viG0pdSZTPk3CDzDv7WyV2mlLq4HuynfV/k/2Wc0ZqBnc8pVTi60oppZYvVyokpOwdwUcfLbUtJ+6Qq+zTSv3epex++6Gh1CiSVbM2zlKYATX6u9Fq4qKJauKiiSpkVoiKfC9S5RXqt52XL5ffOu1f8/77lttZv16vdQkJkeNQc+6cUgMGWP7PX39dqQkLJyjMgNp6Vq/uP5txVmEG1PD/DbfYfkKCvL+/v9zlLaMSNTpKyR1PQM4FpRmNSvXvL8tXrHDiva347TelGjeWc2NcnFLz5sl7z5ghyypiMsm51M1NqTZtlJo5U6lvv1VqzhylnnxSqfR0G29++EOpdV7/N6Uyjyp1eZ9Sx75QattjSh2YpZSS89/DD1v+z93clPrsM4c+Zv1zZpH++7ykk1IrBsq+/M6gVN4Fm6svW6ZUcLDztSKpqUrdfLPlun5+Uttjt3KO14MHlercuWzZwsL012zeLDUi2jIfH6Xmz3fgvSvw3//KdaL5e3t6yvm2Kvz8s1JNmlieFz/5RMn1yvYn9XPG9+769N5XbG63MrTvsqenUk2bKvXII9KS5dFHlRo0qPLbX7xYPrOPj1JTpshvxJw5UkvtRAMth5y4dEL5vu6rIt+LVEO+GqKGfDVERb0fpXxf91UnLpVTbViKQSmtZ7ltBQUF8PPzw08//YQ77rijZP6ECRNw5coV/Kql1DDTsmVLxMXF4WmzhoLTp0/HokWLsHfvXqvvk5+fj3zzXpsAvL29UVjojVWrJBnBxYtyR6xDB2DYcCNS8k7hdMZpFJmKEOgTiPTcdDT1bYq2Tdvi5OWTWHpsKUa2GYleYXLrKiU7BV/s+gIxITG4tf2tJe+TlSVtm319gfvvl7tiMzfMhNFkxMuDXi553Y5zO7Do0CLc3OZmXNfyupL527dLv6F27eQOpbu73DXbvl3uMmRkSITs5iZ3yseMkbvlKSl6f6PMTFnWqROA1sux/dx23N/tfkQFRsn+KcrHrE2zEBEQgYndJ1b8T0v7U9JBN4gEfMMkuUDOKRl4M+QGIKi4oXhRroyXU5glKS6b9tW3oUySJebCRgnfG7YvvisVqL/GfGCM0oquSvvjrCMylk5QP6BpLL7Z/y1OXTmFf/T7Bxp4ye3NMxlnMG/vPPRq3gsj246U9bNPAGcXA4UZ8hlCbgAattbLnbYeuLxT7rz7hAJN+0iihOKap3PnJI3thQuSRW/kSLM7E8YCyZSScVDG+GnUCQgehA3JO7D21Fr8rePf0KmZ3C5Jz03Hpzs+RadmnfC3Fj1kv7r7An7h8t6FmfLwjwZ8QmSf5ZyWu3duXlJ+41Upf6D0yjQapWwHDkjZ7rzTLAFF5hHZRn6apCL1DgICOmKnsQGWHl+Jm9vcjN5h+h2jdza9A3c3dzzd7+nifZMjtUnZxwHPAKBprOx7rUau4LL8X/JSJfNPs+ss7ioqJVl9tmyRu+GjRxenB1UKuLJXxjbKvyg1fp4BgF8LXGzUC7P3zUeHoA64u9PdJdv6ZPsnyCnIQVyvSXC/uFn6fzVsU7xfMuW49PAHQoYBFzfLvoWb3O1UCshLARq2AyJk5LfCQumvsXu3fMe6d5e7khZ3tSs6JiuiTEDqWknqYHCTLDqhwwFP/TbkxYvSvjwjQzq033ST5R3wY8fkDmlBgfTNGDSokne+lEn2d26ydHT1DJDvamEmYCrEUfcQLDj8Owa0GIAbWt1Qsto3+74p8x2zJStL+o7l5cld+NJ38bTvk7e3pG4PDJQ7bln5WfjXoH/B3U12xIrjK7D+9Hrc1/U+dGzWsWT9ffukJu766633Q9y9WxLDjBtnmfofkP357bdy5/Lmm6VfzILEBdifth+T+0xG84bSGzunIAfxG+IR2SgSj/R6xGIbP/8s2+3bF2XZOmZsLL9yRbb/4IPW/9/Hjkl67XvvdXzb6emybV9fOW9o/SAKCiQb3ZEj8hp3d/l8N94oSQU0GzfKHfx27WQIBa02Kz1dzj3nzunJCLTXVJiG31ggd66zDssYIe6+UsOee1YSeoSPLnnp7t0yNpK3NzBqlGx/1sZZ8PbwxlOxT+llTNqINafW4M52t6Jz4Qn5bWoaK8f71XPSL9SUDzS/BfCVJhCFhdJnIDlZEviMHGn5XUxOlgxg2dlyTr3pJvuHF9i7V/p7XLgg5+2wMOl7FBWF4t+JNZLspfAK4NUUCOwmd/Hd3GV5+iap4VJGGX8ueJCcLwD5/qas0H/r/FsDwYORfDkMq1dLeQcOlNoP7X9lMOjfx8JCqVHYsUPOI+3bSy23lu0OkOuJhQvlWunuu8smbVq1Sn7fg4IkqVNQcBE+2vYRPN088VDPh2CAHJPz9s5DVn4WpvSdAm8Pb70wgNXjVSm55lm7VvqKtGol31fzGt30dOC77+T4vesuy7G07LF1qxzTzZpJ2c1ribKzpY/p6dNyXtUGl9akpsoxcemSfFdGjjQbCDT/oozfl3tahmNo2Fb+b2a//+fPy29kYaFc51mM05VzWlqZ5F8EfIKBoIFyXNh7Asi7ACT9AKgiGYNHuxZTShIwXdkj/Z2h5NrCvzUQMhQwuCE9XX47z52T68jgYKntNf+tO3VKfn+NRmndEhNTvODyHskQV3BJjl2DAfAMlGvERp1LfmeOHpXvg4eHJC7qO/gyfjj5Mdo3bY97Ot9T8j4/HfgJh9IPYWLnJ5C4vSmSk2VfBwXJukpJGWJiKq49/uPoH9h0ZhMmdp+INk3aAACOXTqGr/Z8hQEtBuCWtreUv3IxhwKdc+fOITw8HJs2bUJ/s3yZzz33HNatW4etW7eWWcfLywvz5s3DuHHjSuZ98sknePXVV5FazvD0M2bMwKuvvmoxr1evXggz/9V2gNFgxOZ2m5Hhl4GQjBB4Gj1xLvAc3JU7rj94PXwLK8gcVklXrwZh/fp3UVAQiB493kVo6CZ4eBRAKaCgIBAeHtlwdy8qd/3jwcdxoMUBdDvVDZEXIwEAGb4ZWN9pPcIvhqPnKb0H7OLfJLPWbbfeanVb5WkVmIHnr9uJsIb6SFhbz4bg/S09kFPougFn9rXYh9PBpzHg8AA0zZaelUlNk7A3ai86n+mM6LRoG1uonK7B6Xh24E4E+hTAaAKMyg1e7iZcyfPCmN8GY02XNfAq8kJAbgAAINc7F5m+mRh4eCCa5NRcjupLDS5hY4eNaHmhJWKS5FfqqudVrOq2Cs0ymqHfsX42tzG63QlM7H4QXu4mi/k/HWiD/+3tWM5a9tkevR1pjdLQOFvOHMqgcKnhJbQ/1x7tzrt45EYHOPt9OXbsThw5MhZGo37VEBBwAoMHPw2j0RN7905BcvIgAPqvd/PmG9G791t2v7ejZbvc4DI2dNiAFukt0P1095L5azutRZZPFkbtGgW34twzFW07K6sFtm9/ETk5EQAAN7d8dO/+AcLDNwAALl3qiB07XkB+vvxvvb0vITb232jU6FruXe56eXlNsHnza8jOlsgvIOAk+vX7F7y9bWflUMoNhw7dh2PH7oKWfygoaA/69HkTHh4Vd3zXOPtdqcjmtpuRHpCOG/fdCJ9Cidq2td6G1MBU3LD/BrRwd0egTx6UMiCvyAPahYoBwPlsCdovXuyIvXv/XnK8ArJvBg58Fu7uRUhMfBinT4+AUp4WywcPnlrhZ8vKisCOHS8gO7slmjffiKZNE+HmVoirV5vB2/syhvb4Hi9ctxMhDXKwOyUYqdl+6BB0Ca2bZGL29q7YmxqEfw3ahoiAHGQXeOLSVW+E+ufCw82E51deh8MXKx4rp6DAH4WF/jCZPODubt5e3wQ/P/uSomRnh2PLlhm4elUCQn//JPTv/wp8fC5XuN6lBpewtc1WeJg8EJ0ajZPBJ1HgUYC+x/siKKv60sCWd8wZjV5ITHwESUk3Qjuevb0vIzb2VZu/QyaTGxITH0VS0o0Wx4S7+1XcdNNE3N0lEQ90OwjAgBOXA2BSBnRsdhlpOb54ePFwl3+2/hHnMLnvPgR4682ld58PwuwdXfHS9TvQ3D8HX+/riEPpjeHjYcTU2N1o1iAP43662ea1mlLAwYMTcPLkbTCZ9Ne2bv0z5j78FAZFnsNXezpiyZEotG6cgesjz6FfRApWHm+J7xPLb1apoLCi2wq4KTfcuP/Gkvkru66E0c2IG3bcgfQLPZCX1xQNG56Fl1cmAO26ww0BAadgMJgq3C8VWbx4se0XOVKFlJycrACoTZs2Wcx/9tlnVd++fa2u4+npqb777juLeR9//LEKDg4u933y8vJURkaGxSPPmV53Zs5nnVfh74SrG+bdoP65/J/K6zUvtSlpk+0VK6moSKn4eKWuv16p226TjoRvvy2P+HjbzQMSTiQozICaunRqybyv936tMANq1sZZli92pqlOUZ5Si6OtNw9L+sX2+pXw5e4vFWZAxS2LUxuTNqqNSRvVmB/HKMyA6/83RVf1JnUHZsnfJqMkM5jvqZRS6u2NbyvMgHp307tqyeElCjOgJi6a6Npy2aHQWKj83/RXIbNC1COLH1GPLH5E3fLtLQozoN7a8JbtDaRv1ZNMHJmtVNYJpY7NkX2x6YFKl+9c5jnVKL6RGjZvmMrIy1CR70U63HmwWjjxffnf/yybW3TpIs2G/Pxk+YMP6ss7dJDvfGioUn36OPjeDpZNa6o14usRFvObvNVEhb0TZvnicrZ9+bL15jCxsbL85En5zKWXP/ig3cUkJ+TnK9WuXdn9/pSd/ZvLSwKxcqUDhXDm3GLDB1s+UJgB1fzt5qrV+61Uq/dbKc9/e6qOH3W0a/3k5LJNlLRHSopSL72k/925s1ITJyrVo4c07bH12YYMkVmTJpXz5stj5ffyxNfyd8ZBabL3HZQ6/JGeSGf3c3qynI3jZF7KGrv3kbPK+y7be8zsPLdTNX2rqer+aXfVKL6R2pi00aXltaqcY+7xx63/z2fPtr1J7ZgwGJSaMEG6Cbz6qlItWyp15dR++f/8HCRNMTXfQalfQqvsYymlrH+2zGNyTrZ2LfbXTMtkQkVXldr9gv7QEhZVID7e+n4bPlxJF4N1tyq1ZqRS+/8t10QH3lbqr3ilso7b3PYDvzygMAOq1futVOsPWqvoD6IVZkDd97OD/S9c8DujlFIOjaMTFBQEd3f3MjUxqampCNVG1iwlNDTUodcD0kzNu4qHjQ71D8Wv9/6Kf635F/an7ceXt3+J/i2sjOJWxdzdpZO0eUdppeyvxYwJkTv2c3bNwcJDCwEAmfmZFssqJekHaRoGAJHjpEnA4ffsyudfWbHh0mzu3S3v4t0tescyTzdP9GhuZajxqpS8BCi4KNXCHf8JFFwBjv8XMOl3z6b2m4q5e+Zi9o7ZaNOkDQJ9AvHW8LfK32Y18XDzwHUtr8OyY8swZ9cci2VDo+zIl3viKwAKaDsFaPu4NCPzqLreoc0bNsesG2fh0SWP4rbvb8OZzDPY+OBGeLq7rnawumgVzT16SNOHiAhp6vXqq9Ic4ssvZfnTT0tiCTc3aZrx88+uLVfzhs3h4eaB5Cw96UReUR4uXb2EvuHW2miV9cEHFQ8i+O9/l00LTa43b540Syvt6lXb62ZmVj5tr6uMbjcaU5dNRXZBNiIDI5Gem45CUyFGtxtte2XIeHVXrsj0E08AkyZJE+BXXpHm4h9+KMsmTQK++EJv/mVPp3ut+XBKit7U3MKl4kE9Q4qbiZ6YJ8l6ggcD7t4yjIKbF9DtdWnGtv1x4MKfdn2uqvDhh9a/y6WHCyhPz+Y9kfLPFBSZiuBucK81v90nTgBzik95Pj7yfy8slCRDthQW6kN+vPCCDFmi+ec/Ae9jv8gfEXdKs+pLu4ENd1btB6jIkQ+lGbhPKND/a2kCueEuIHW1NCvv9jpw8n/AilggaIAkHMpPl2ad7uXk8y6Wmwu88YZMd+wo56l27aQJ2x9/AAi9QR5OGt1uNL7e9zVyCnMQERCBs5lnS+Y7xNFm5nZyKNDx8vJCr169kJCQUNJHx2QyISEhAVOmTLG6Tv/+/ZGQkGDRR2flypUWTd+qS6+wXlh6XxWkFqkkR9rqN/VrivCG4UjOSkZSRpLFspjQKgh0UlbJc8SdkkELkD4S1RDodAjqgADvgJLATdMtpBt8PCr+4lZabvG+9Jc2n8i/CPz1ulzwu8nIYx5uHvho5Ee458d7sC15G9644Q0ENwguZ4PVa2jUUCw7tgxTY6fi+pbXY8rSKcgtzEXP5j1tr3x5tzwHFQ/0cfhdYP/0Ki3fwz0fxonLJ5Cem457Ot2DfhG2m9PVdseOAcePy/Sbb0qQA0jb8LfflpOHUtIefsYM/eLIy0v6m7iSm8ENof6hOJt5FscuHQMAJGdK0BMREFHRqiW++kqePT2Bjz+W9tuffQZs2CAnyh9/lOUeHsBbb0mb+vj4qv4kVJo9F3Hl+eknycgFyLg+n30m/RbKOV1Xq+jG0egY1BFpOWnY+OBGvLL6Fby54U27L460wSnvvBP45BOZ7tNH+qls3Sr9NAwGuQlhHqiMHGl72598IjcpFy+WMfsGDJC+UadPy6CXb954o/TtPDkP6PyijN8GSL/IrOIbh27e+lhyDaKByOKBmhtE2vX5KsN8bJPISOm3s2+fY9vwcPOAh1vtGlP+11+lX4fBIH2+tD5+U6cCBw9WvO6GDfqAxE88YbnMzw+AZ3EfxsLi5qANWwOxX8m0e9XeeLcqbZ08d/239AkFAC+zJvKdX5ZHUY700VFF0k/aq+JmkIBkJs0uHhZx3jw9u+X48ZIdr7JGtB4BDzcPdA/tjuX3L8fIb0di5fGVGNF6ROU3XgUcPorj4uIwYcIE9O7dG3379sX777+PnJwcTJo0CQAwfvx4hIeHI774DDh16lQMHjwY77zzDkaNGoX58+djx44d+PyazzFpv5jQGCRnJaN7aHf4ePhge/J2NGvQrGouui8XD48cdX/lt+Ugg8GA3mG9sebkGpx6+hROXD6BofOG2n0HulL8i/v/ZBb/OjZsDdx5vszLhrYaivTnat9AkVrNjbvBHcOihyEtJw23tL2lpCN4hUoP5Bpxp5yIAX2/VJLBYED88Pp1Fbx+vTx7elofYHjbNnnu0sWyQ3B1iQiIwJazW9D2P20t5ze0HeikpkonVUDSvz5S3H9/1ixJPLBnj36inDlTH9F8xAhg7twqKT5ZcemSXKQAkiRg5UpJ3/vYY/atv7l4XL1GjSS9fmCgHJ+dOgGXK+6qUS1GtxuNWZtmYee5nVh+fDka+zTGwBYDba6XliadogFg4kTLZaGh+kVvaGjZhBb2aNZMBkTMydE7X5tMMgxFx44ACj4Htj0C7HsJODFXfjcLMyRJy8i/5O/sE5LsJeIOoNNzsuGiHEnc4EJHjshNGUBqnteulWQyH30EHDrk0rd2Oe03tk8fy0QmbdrIoyLni0/v7u76TSoLUfcD+18BzvwEnLwFaHG3JCHIPOjQ8AxOKcoBMoqHXGlRHHkUXLFoYVLCo4HDx9DatfLcpk3ZFP7+/g5tyqpGPo0wsMVArD+9HlfyrmD96fUY0GIAGvvaDsKqg8OBztixY3HhwgVMmzYNKSkp6N69O5YtW4aQ4kFgkpKS4GZ2+2TAgAH47rvv8K9//QsvvfQS2rZti0WLFqFLly5V9ynquZiQGPxx9A+8N+I99GreCwEzA6qm2ZoxX6rbAckMAsh4JVf2l79OFYsNj8Xqk6txJuNMyZhE1RLohI2SKuLLu4E9LwId/iEZ4S7vln0SPdH1ZaiEns17IsA7AH8m/YmNSRthUib7mq0B0tziwp/A2YVA1DjJAFecBQ7KVPG61zBtLIHQUOvjr2iBQEnWvNKsVeXaqt51IHNceTU39tTomI9cP2aM5bI77tCbixgM0hRI4+cHTJ5sc/PkpMREucAGpPZMG9/q++/1ZjgV0f6vN90kQY6mVSvHs1y5wqi2ozBr0yx8t/877E7ZjTGdx9h1s2bPHn26t5WhSrQmlgEBlStfgwaS0bEMz5bA0OWSYSvrqLQI8AoEGnUF/MKAHu8CG8cAG+6Wc02DKGlFcGET8DfrSZiqija2EiDNZ7V9MGWKZCmry7RAx55audK0/WA0SpBfepwr+IYAQ5YCu/4BbBkPbJkgWTeVEej4HNDcyt2tqpJzWt7HoyHgLYmZ8EfnqhmXDvpNgbZtK35dZYxuNxrrTq9D/J/xyC3MdbzZmgs5VS85ZcqUcpuqrdVCRzP33HMP7rnnnrIvJrtoQU1iWiK8i6tQqyTQKbgiXy5A/3IdeEvSF1YTLajZeX4njlw8YjHPpdw8gUG/AhvvBQ7OlIemxT21PtBxd3PHoMhBWHZsGZYfXw7Azv45ABA9CTj0DnDmRxkYM2yU1O6c+x2I/D8gcqwLS153aSkwS2W+L6GdSMvt5+Ki9seaFgHWb123aGT7lrZ2QRwUJHf8S/vrL3nu2rXsBUJ1DBh3rdLuzAOSHljj5QU8+WTF6yql/9+G2vnTUN0GthyIxj6NMXvHbJiUCaPb2ndxlJGhTzdrVna5Nu/cOcf6xDqsQaT1pmgRtwO3JMqNw/TNcvPQLwKIebPsa6vY4cPy7OsrqbDNDbRdWVazbNwMSvKQ31CrNTI2XH+9pDfPz5c+k1qttYXgwcDNu4Dcc0DOSblO8I+WoR0qq6LPdrG4ZY2Xa5oC5BaPm1wmuKtCo9qOwrMrn8WH2z4s+bu2qF0NMMkqrS/OX2l/lfRdKemfY+sucUUXV1q1qJsXUENtcbWEBFqgE+AdgA5BHarnzZv2BW49LieijP0A3GQcmaDq7z/mjKFRQ7HkyBLM3T0XjX0a299nq0EkMGSZ3LlK+kEegOTM7/CMy8pbK1T0fbERiEQWX89cuCAJCEqP7t2vn7TtT0yUJkeuPKlYU5kaHe2COirK+i7Smrx07uxk4cgp2v8lIkJqEs152PjJTknRaxlrQ+2NNR5uHhjRZgTmJ86Hu8FdHzvNBvNxN4zGsuPiDBsmz1lZMt7WiJroKtCwLdDllWp/Wy1xRc+e9o8XVBcUwqMkmULTpo6v36iR1E4vWAA895zUyg8ZIuMyfvSRJKwoGUvHL0weVami80tGcTVcwRV93sAfZcwoQG9x4SRt/CStj5IrdGzWEdGNo3Hi8glEBUahc3DtOVkw0KkD2jZpC18PXyReSCwZrKtKanQ8io9+U4EMvOnhB1z3gwzwCQD+UZV/DxuaN2yO8Ibh2Ja8DUkZSegX0Q9uBivtglzFYACaDZBHHaPV4OQU5uDG1jc6tt+aDQRGbJN25LlnpUNjQMcaC3jrghtukJOjySQD3Zn3k8jLk+YUbm6SDeull+Tk6eEh7fwXLJBBJF1JC2ge7P4g7u50N+I3xOPPpD/tCnS0DuvmzZvMadmtyltOrqE1OelpR46R0rT/KWB2AVcLvXz9yxjWahgaeTdCE1/77g6YX+impuo3ITQdOkhfmoMHpcnW3LlAbKwMAPrOO9L/pr7SgmOLQSzrigqCAQ8FGNzlJVoNhaP+8x+p8dqzB3j4YctlX3zh3DarRINI+XBF2TJgqE+zKr0maV88DI5W2+cqH978IQ6lH0L7oPLH3akJ1XhFSc5yd3NHl+Au+Cvtr5LmayUHkvXU6PqjIl6NZTR6QC52AbkL1bibPDwr2cDZTrERsTiUfgi5hbnoG1YNzdbqiZjQGHQN7oqowCjnq4n9o6XDZWDXayPIcfa7Aqmh0ZqCvPgi8M03wNmzwJIlcmcwKAh4/HFZ/tln0vFz2DC5G29Pf4rK0gKaxr6NMbLtSBgMBhhgQHjDcBtrSgpsQPokWKP1eWCgU73OFTfRL7ffVwXMm1hWRYdjV+kS3AUP93zYYlR1W3r21Gt1rLSWBwDMni1NlY4dk47r3t4yQvyqVZUvc22WWZzEtLL9k2obg0H//blwwbltNGsG7NghCVbuvx+45Ra5YbVyZQ3fDPBoIP27AGlSbk7LAlcJQ4bI84kTeoISjXa8VIVR7UbhmQHP1Kr+OQADnTojJiQGl/MuY9OZTegc3Llq0j4a3IDG3WX6/LLKb89J5sFNtfTPqSfcDG7Y98Q+nJx6Eg/3fNj2ClRp774rbd8vXwYeeEAyOt16q97H5f33Jdjx9JRUtKtXS21Iddxd1froaGPpJGcmI8Q/xK4xMLRhy7LKOadqyRcKC60vJ9fQAszSzSTtYT4UXXn/17qqYUMgprhRw6ef6gkbALmQzcgABg+Wzvd33aUHRRERwLPPVn95q1NVJWKojbTEEFu2OL8Nd3fg9tslBffvv8vxM3x4lRSvckKGyPP+6UDyb0DBZeDsr0DC4EpvesAA/XiYMAFYt06atn7+edmarfqIgU4dofW/uFp0tWqarWmaFOcaPPIf4GqKTBvzge1P6OmHXWxgy4EIbhCM4AbBiI2IrZb3JHJGr17AmjXSjE3j66s3Y/P0lDvJJ09Kc5lPPwV27pTaH1dr3rA53A3uOJcl1QDns8/bPYaOdiGtNVErzde34uXkGlqtjDMXrea1OLUhlXRVu+sued6yRWpaP/1UxlO57jr9Yr9XLxlLKC9Pgp8zZyR9en2mBX31qX+OJrb48mDpUsuaCKXkN7dOa/cU4O4rg4Cuvw34uQnw5x1A1jGbq9ri4wNMmybTR49KDU/z5nLeqo+/DaVdA21V6gfz4KZKA52WY4DD7wHZx4ClXaUj/qUdwNXzQM/3qu59KnBdy+uQ+k/XptwkqiqxsUBCglw0XbkiTdS0QEATHm6Zhrk6eLh5IMQ/BOeyzuFK3hXkFubaHehoNU7Hj0vnbvPO3oDeEV5LsU3VQ0sMYTQ6vm5IiASwWVnSZKW+eeopGfzwyBFpjlO6SY45D4/6WcNhjVaTV5VNkmqLwYNlHK/MTBko9vPPpdnt9Oly80lrOlwn+bcCBsyX8ZnyzVJ3Nu1T/joOeOYZCfbfflv6kQLy+6KlrK/PWKNTR8SExqBzs87o1KxT1dZ6BPWTdMqA3ElI/k2CHCKqUIsWkm65dJBTkyICInA+63xJrY49g4UC8jkAORHu2lV2eadO8qyNY0HVw0eSbDp10Wow6KnCV6+uujLVFv7+0t9Gq9kB5DOPH1+7ky+4mpZhqz4GOiNG6JkfV6+Wm0ydOgE//ljxenVGxG3A6CNAn8+AHu8Aw9YDNyRU2eb//W+5MfD559Ly4MAB4LXXqmzztRZrdOqIAO8AJD6Z6JqN9/8a8G8NHJol4+r4hAJdpgHuPq55PyJyiYiACGxL3oYDFw6U/G2PrmbZS//3P8vRs+fM0S8uzp2TVNMdzDLAX758bV9YupJ20epsH5uuXaWmY8UK4OJFPVvZsWNSG2ltsM0SlRm6oJq0aCFN0w4fBpKTJbtUuO3cG/Vay5bA1q3A+Xp4v9JgkFq8m24qmyq5NifccIhXI6DNoy7bfEREOWMI1WOs0SHA3RvoHg/cdQm47RRwRzLQ9omaLhUROUhLSLA9ebv8bcdgoYBklOvYUaY/+gh45RXpizRuHPDf/1oOIjptmt4PYO9e4PXXq6z4VErLlvKsZV9z1IDiDLXZ2XI3fMcOGSxx6ND61d+qfXtpumR3kGMw6I+K5tVBWjPU+lr72quXpA0fPx6IjpamxHPnShY1ImsY6JDOM6A4nzsPC6K6SKvB2XF+h8Xf9njoIX369dflwlEbb6RzZxlMFJBmIjfcINl7+vevXxfMtU2bNvK8c6dz6991l55oYudOqam7+25Ji071kzZmSmqqPqaOpr70sQsOlpqd48clGUV194ekuoVXtERE9YQW2Ow8t9Pib3s8+aTcIS1Nu8n9qFlrinXrpImb1qmVXKNtW3m+dEkf8V5jTx8Mf3/gX/9y8s0rM0Yb1Rgt0AGk47nm0CEZLJXoWsNAh4iontACm4z8jLKDhdpoquPrC/z5JzBwoP6SZs2AN9+U6aef1vvqmKs3beNrIa1GB7AcuT0tDZg1y75tPPusdDh2MzvbjxqlN2uj+iUmRs8w99lncgNj5kwZNLU+JiggsoXJCIiI6gnzGpwgvyB4e3hX8OqywsJkpPmEBKmtGTZMb/rk6wts2iQDpS5eLPHR2LHAG29U4QcgC126SOa1vDy5Ox8RIbU8zz0nzQbtYTBIrc7NN0u/jfbt5f96TavHNVLe3sBtt+ljd82eXbPlIappDHSIiOqJ8IbhcDO4waRMdiciKM3DQzquWxMQAPz6q3SO9/SUGh9yHT8/GbV9yRK5Np86VV9mb6Cj6d3bRpY1qjeefRb49tt6Hc8R2Y1N14iI6glPd08ENwgG4Fj/HEeFhTHIqS6PPVbTJaC6pls3yZ5o3lzRwwMYPbrmykRUUxjoEBHVI1qAU2awUHYsr5NGjZJmZ6Vd6+PFUMWefBL44QfpmzNypAywOXJkTZeKqPoZlOJZjoiovkg4kYDkrGTEhMQgJjSmpotDVSA/X9JCL1kif//f/wFffSXNB4mIqHx1rkYnPz8fM2bMQH5+fk0Xpc7hvnMO95tzuN+cV5l9Nyx6GMbHjL8mg5z6esx5ewO//aZXwH37bdUGOfV1v1UH7jvncL85h/vNcXWuRiczMxONGjVCRkYGArQcimQX7jvncL85h/vNedx3zuF+cw73m/O475zD/eYc7jfH1bkaHSIiIiIiIlsY6BARERERUb3DQIeIiIiIiOqdOhfoeHt7Y/r06fD2dmzEb+K+cxb3m3O435zHfecc7jfncL85j/vOOdxvzuF+c1ydS0ZARERERERkS52r0SEiIiIiIrKFgQ4REREREdU7DHSIiIiIiKjeYaBDRERERET1DgMdIiIiIiKqd+pcoPPxxx8jKioKPj4+iI2NxbZt22q6SLXO+vXrceuttyIsLAwGgwGLFi2yWK6UwrRp09C8eXP4+vpi+PDhOHr0aM0UtpaIj49Hnz590LBhQwQHB+OOO+7A4cOHLV6Tl5eHyZMno2nTpvD398ddd92F1NTUGipx7TF79mx069YNAQEBCAgIQP/+/bF06dKS5dxv9pk5cyYMBgOefvrpknncd2XNmDEDBoPB4tGhQ4eS5dxnFUtOTsb999+Ppk2bwtfXF127dsWOHTtKlvP8UFZUVFSZY85gMGDy5MkAeMyVx2g04pVXXkGrVq3g6+uL1q1b47XXXoN5sl8eb9ZlZWXh6aefRmRkJHx9fTFgwABs3769ZDn3mwNUHTJ//nzl5eWl5s6dq/766y/1yCOPqMDAQJWamlrTRatV/vjjD/Xyyy+rX375RQFQCxcutFg+c+ZM1ahRI7Vo0SK1d+9eddttt6lWrVqpq1ev1kyBa4ERI0aoL7/8UiUmJqo9e/aoW265RbVs2VJlZ2eXvObxxx9XLVq0UAkJCWrHjh2qX79+asCAATVY6tph8eLF6vfff1dHjhxRhw8fVi+99JLy9PRUiYmJSinuN3ts27ZNRUVFqW7duqmpU6eWzOe+K2v69Omqc+fO6vz58yWPCxculCznPivfpUuXVGRkpJo4caLaunWrOnHihFq+fLk6duxYyWt4figrLS3N4nhbuXKlAqDWrFmjlOIxV5433nhDNW3aVC1ZskSdPHlS/fjjj8rf31998MEHJa/h8WbdmDFjVKdOndS6devU0aNH1fTp01VAQIA6e/asUor7zRF1KtDp27evmjx5csnfRqNRhYWFqfj4+BosVe1WOtAxmUwqNDRUzZo1q2TelStXlLe3t/r+++9roIS1U1pamgKg1q1bp5SSfeTp6al+/PHHktccPHhQAVCbN2+uqWLWWo0bN1ZffPEF95sdsrKyVNu2bdXKlSvV4MGDSwId7jvrpk+frmJiYqwu4z6r2PPPP6+uu+66cpfz/GCfqVOnqtatWyuTycRjrgKjRo1SDz74oMW8v/3tb+q+++5TSvF4K09ubq5yd3dXS5YssZjfs2dP9fLLL3O/OajONF0rKCjAzp07MXz48JJ5bm5uGD58ODZv3lyDJatbTp48iZSUFIv92KhRI8TGxnI/msnIyAAANGnSBACwc+dOFBYWWuy3Dh06oGXLltxvZoxGI+bPn4+cnBz079+f+80OkydPxqhRoyz2EcBjriJHjx5FWFgYoqOjcd999yEpKQkA95ktixcvRu/evXHPPfcgODgYPXr0wJw5c0qW8/xgW0FBAb755hs8+OCDMBgMPOYqMGDAACQkJODIkSMAgL1792LDhg0YOXIkAB5v5SkqKoLRaISPj4/FfF9fX2zYsIH7zUEeNV0Ae6Wnp8NoNCIkJMRifkhICA4dOlRDpap7UlJSAMDqftSWXetMJhOefvppDBw4EF26dAEg+83LywuBgYEWr+V+E/v370f//v2Rl5cHf39/LFy4EJ06dcKePXu43yowf/587Nq1y6LttYbHnHWxsbH46quv0L59e5w/fx6vvvoqrr/+eiQmJnKf2XDixAnMnj0bcXFxeOmll7B9+3Y89dRT8PLywoQJE3h+sMOiRYtw5coVTJw4EQC/pxV54YUXkJmZiQ4dOsDd3R1GoxFvvPEG7rvvPgC8HilPw4YN0b9/f7z22mvo2LEjQkJC8P3332Pz5s1o06YN95uD6kygQ1RdJk+ejMTERGzYsKGmi1JntG/fHnv27EFGRgZ++uknTJgwAevWravpYtVqZ86cwdSpU7Fy5coyd+6ofNrdYADo1q0bYmNjERkZiR9++AG+vr41WLLaz2QyoXfv3njzzTcBAD169EBiYiI+/fRTTJgwoYZLVzf897//xciRIxEWFlbTRan1fvjhB3z77bf47rvv0LlzZ+zZswdPP/00wsLCeLzZ8PXXX+PBBx9EeHg43N3d0bNnT4wbNw47d+6s6aLVOXWm6VpQUBDc3d3LZDJJTU1FaGhoDZWq7tH2FfejdVOmTMGSJUuwZs0aRERElMwPDQ1FQUEBrly5YvF67jfh5eWFNm3aoFevXoiPj0dMTAw++OAD7rcK7Ny5E2lpaejZsyc8PDzg4eGBdevW4cMPP4SHhwdCQkK47+wQGBiIdu3a4dixYzzebGjevDk6depkMa9jx44lTf94fqjY6dOnsWrVKjz88MMl83jMle/ZZ5/FCy+8gHvvvRddu3bFAw88gH/84x+Ij48HwOOtIq1bt8a6deuQnZ2NM2fOYNu2bSgsLER0dDT3m4PqTKDj5eWFXr16ISEhoWSeyWRCQkIC+vfvX4Mlq1tatWqF0NBQi/2YmZmJrVu3XtP7USmFKVOmYOHChVi9ejVatWplsbxXr17w9PS02G+HDx9GUlLSNb3fymMymZCfn8/9VoFhw4Zh//792LNnT8mjd+/euO+++0qmue9sy87OxvHjx9G8eXMebzYMHDiwTNr8I0eOIDIyEgDPD7Z8+eWXCA4OxqhRo0rm8ZgrX25uLtzcLC8z3d3dYTKZAPB4s0eDBg3QvHlzXL58GcuXL8ftt9/O/eaoms6G4Ij58+crb29v9dVXX6kDBw6oRx99VAUGBqqUlJSaLlqtkpWVpXbv3q12796tAKh3331X7d69W50+fVopJWkJAwMD1a+//qr27dunbr/99ms+LeETTzyhGjVqpNauXWuRRjQ3N7fkNY8//rhq2bKlWr16tdqxY4fq37+/6t+/fw2WunZ44YUX1Lp169TJkyfVvn371AsvvKAMBoNasWKFUor7zRHmWdeU4r6z5plnnlFr165VJ0+eVBs3blTDhw9XQUFBKi0tTSnFfVaRbdu2KQ8PD/XGG2+oo0ePqm+//Vb5+fmpb775puQ1PD9YZzQaVcuWLdXzzz9fZhmPOesmTJigwsPDS9JL//LLLyooKEg999xzJa/h8WbdsmXL1NKlS9WJEyfUihUrVExMjIqNjVUFBQVKKe43R9SpQEcppf7zn/+oli1bKi8vL9W3b1+1ZcuWmi5SrbNmzRoFoMxjwoQJSilJ6fjKK6+okJAQ5e3trYYNG6YOHz5cs4WuYdb2FwD15Zdflrzm6tWr6sknn1SNGzdWfn5+6s4771Tnz5+vuULXEg8++KCKjIxUXl5eqlmzZmrYsGElQY5S3G+OKB3ocN+VNXbsWNW8eXPl5eWlwsPD1dixYy3GgeE+q9hvv/2munTpory9vVWHDh3U559/brGc5wfrli9frgBY3Rc85qzLzMxUU6dOVS1btlQ+Pj4qOjpavfzyyyo/P7/kNTzerFuwYIGKjo5WXl5eKjQ0VE2ePFlduXKlZDn3m/0MSpkNUUtERERERFQP1Jk+OkRERERERPZioENERERERPUOAx0iIiIiIqp3GOgQEREREVG9w0CHiIiIiIjqHQY6RERERERU7zDQISIiIiKieoeBDhERERER1TsMdIiIiIiIqN5hoENERERERPUOAx0iIiIiIqp3/j/DL9cHIzLejQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1000x200 with 1 Axes>"
       ]
@@ -5764,19 +561,12 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "d6695d34",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:23.472884Z",
-     "iopub.status.busy": "2025-12-20T01:14:23.472759Z",
-     "iopub.status.idle": "2025-12-20T01:14:24.295864Z",
-     "shell.execute_reply": "2025-12-20T01:14:24.295401Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<logomaker.src.Logo.Logo at 0x14bcf4f31050>"
+       "<logomaker.src.Logo.Logo at 0x7f1e19f53050>"
       ]
      },
      "execution_count": 15,
@@ -5785,7 +575,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x200 with 1 Axes>"
       ]
@@ -5803,14 +593,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "945a1fc4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:24.296978Z",
-     "iopub.status.busy": "2025-12-20T01:14:24.296848Z",
-     "iopub.status.idle": "2025-12-20T01:14:28.354510Z",
-     "shell.execute_reply": "2025-12-20T01:14:28.354070Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5847,7 +630,7 @@
        "      <td>pos.chr9_133251979_C_T_ABO-ABO@24040</td>\n",
        "      <td>74040</td>\n",
        "      <td>74054</td>\n",
-       "      <td>1.062168</td>\n",
+       "      <td>1.062003</td>\n",
        "      <td>0.000249</td>\n",
        "      <td>24040</td>\n",
        "    </tr>\n",
@@ -5856,7 +639,7 @@
        "      <td>pos.chr9_133251979_C_T_ABO-ABO@24071</td>\n",
        "      <td>74071</td>\n",
        "      <td>74077</td>\n",
-       "      <td>0.084875</td>\n",
+       "      <td>0.084775</td>\n",
        "      <td>0.000300</td>\n",
        "      <td>24071</td>\n",
        "    </tr>\n",
@@ -5865,7 +648,7 @@
        "      <td>pos.chr9_133251979_C_T_ABO-ABO@23950</td>\n",
        "      <td>73950</td>\n",
        "      <td>73956</td>\n",
-       "      <td>0.077155</td>\n",
+       "      <td>0.077116</td>\n",
        "      <td>0.000300</td>\n",
        "      <td>23950</td>\n",
        "    </tr>\n",
@@ -5874,7 +657,7 @@
        "      <td>pos.chr9_133251979_C_T_ABO-ABO@784</td>\n",
        "      <td>50784</td>\n",
        "      <td>50790</td>\n",
-       "      <td>0.073198</td>\n",
+       "      <td>0.073299</td>\n",
        "      <td>0.000300</td>\n",
        "      <td>784</td>\n",
        "    </tr>\n",
@@ -5883,8 +666,8 @@
        "      <td>pos.chr9_133251979_C_T_ABO-ABO@755</td>\n",
        "      <td>50755</td>\n",
        "      <td>50760</td>\n",
-       "      <td>0.051404</td>\n",
-       "      <td>0.000338</td>\n",
+       "      <td>0.051431</td>\n",
+       "      <td>0.000340</td>\n",
        "      <td>755</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -5898,47 +681,47 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
-       "      <td>neg.chr9_133251979_C_T_ABO-ABO@479</td>\n",
-       "      <td>50479</td>\n",
-       "      <td>50483</td>\n",
-       "      <td>-0.028738</td>\n",
-       "      <td>0.000431</td>\n",
-       "      <td>479</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
        "      <td>neg.chr9_133251979_C_T_ABO-ABO@19662</td>\n",
        "      <td>69662</td>\n",
        "      <td>69671</td>\n",
-       "      <td>-0.100773</td>\n",
-       "      <td>0.000451</td>\n",
+       "      <td>-0.100774</td>\n",
+       "      <td>0.000441</td>\n",
        "      <td>19662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>neg.chr9_133251979_C_T_ABO-ABO@857</td>\n",
-       "      <td>50857</td>\n",
-       "      <td>50862</td>\n",
-       "      <td>-0.039062</td>\n",
-       "      <td>0.000451</td>\n",
-       "      <td>857</td>\n",
+       "      <th>40</th>\n",
+       "      <td>neg.chr9_133251979_C_T_ABO-ABO@479</td>\n",
+       "      <td>50479</td>\n",
+       "      <td>50483</td>\n",
+       "      <td>-0.028684</td>\n",
+       "      <td>0.000441</td>\n",
+       "      <td>479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>42</th>\n",
+       "      <th>41</th>\n",
        "      <td>neg.chr9_133251979_C_T_ABO-ABO@24184</td>\n",
        "      <td>74184</td>\n",
        "      <td>74190</td>\n",
-       "      <td>-0.038328</td>\n",
-       "      <td>0.000484</td>\n",
+       "      <td>-0.038351</td>\n",
+       "      <td>0.000466</td>\n",
        "      <td>24184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>neg.chr9_133251979_C_T_ABO-ABO@19688</td>\n",
+       "      <td>69688</td>\n",
+       "      <td>69694</td>\n",
+       "      <td>-0.044377</td>\n",
+       "      <td>0.000498</td>\n",
+       "      <td>19688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>neg.chr9_133251979_C_T_ABO-ABO@23624</td>\n",
        "      <td>73624</td>\n",
        "      <td>73628</td>\n",
        "      <td>-0.028224</td>\n",
-       "      <td>0.000484</td>\n",
+       "      <td>0.000498</td>\n",
        "      <td>23624</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -5948,17 +731,17 @@
       ],
       "text/plain": [
        "                                    peak  start    end  attribution   p-value  \\\n",
-       "0   pos.chr9_133251979_C_T_ABO-ABO@24040  74040  74054     1.062168  0.000249   \n",
-       "1   pos.chr9_133251979_C_T_ABO-ABO@24071  74071  74077     0.084875  0.000300   \n",
-       "2   pos.chr9_133251979_C_T_ABO-ABO@23950  73950  73956     0.077155  0.000300   \n",
-       "3     pos.chr9_133251979_C_T_ABO-ABO@784  50784  50790     0.073198  0.000300   \n",
-       "4     pos.chr9_133251979_C_T_ABO-ABO@755  50755  50760     0.051404  0.000338   \n",
+       "0   pos.chr9_133251979_C_T_ABO-ABO@24040  74040  74054     1.062003  0.000249   \n",
+       "1   pos.chr9_133251979_C_T_ABO-ABO@24071  74071  74077     0.084775  0.000300   \n",
+       "2   pos.chr9_133251979_C_T_ABO-ABO@23950  73950  73956     0.077116  0.000300   \n",
+       "3     pos.chr9_133251979_C_T_ABO-ABO@784  50784  50790     0.073299  0.000300   \n",
+       "4     pos.chr9_133251979_C_T_ABO-ABO@755  50755  50760     0.051431  0.000340   \n",
        "..                                   ...    ...    ...          ...       ...   \n",
-       "39    neg.chr9_133251979_C_T_ABO-ABO@479  50479  50483    -0.028738  0.000431   \n",
-       "40  neg.chr9_133251979_C_T_ABO-ABO@19662  69662  69671    -0.100773  0.000451   \n",
-       "41    neg.chr9_133251979_C_T_ABO-ABO@857  50857  50862    -0.039062  0.000451   \n",
-       "42  neg.chr9_133251979_C_T_ABO-ABO@24184  74184  74190    -0.038328  0.000484   \n",
-       "43  neg.chr9_133251979_C_T_ABO-ABO@23624  73624  73628    -0.028224  0.000484   \n",
+       "39  neg.chr9_133251979_C_T_ABO-ABO@19662  69662  69671    -0.100774  0.000441   \n",
+       "40    neg.chr9_133251979_C_T_ABO-ABO@479  50479  50483    -0.028684  0.000441   \n",
+       "41  neg.chr9_133251979_C_T_ABO-ABO@24184  74184  74190    -0.038351  0.000466   \n",
+       "42  neg.chr9_133251979_C_T_ABO-ABO@19688  69688  69694    -0.044377  0.000498   \n",
+       "43  neg.chr9_133251979_C_T_ABO-ABO@23624  73624  73628    -0.028224  0.000498   \n",
        "\n",
        "    from_tss  \n",
        "0      24040  \n",
@@ -5967,10 +750,10 @@
        "3        784  \n",
        "4        755  \n",
        "..       ...  \n",
-       "39       479  \n",
-       "40     19662  \n",
-       "41       857  \n",
-       "42     24184  \n",
+       "39     19662  \n",
+       "40       479  \n",
+       "41     24184  \n",
+       "42     19688  \n",
        "43     23624  \n",
        "\n",
        "[61 rows x 6 columns]"
@@ -5989,20 +772,13 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "0b882954",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:14:28.355932Z",
-     "iopub.status.busy": "2025-12-20T01:14:28.355793Z",
-     "iopub.status.idle": "2025-12-20T01:15:04.649712Z",
-     "shell.execute_reply": "2025-12-20T01:15:04.649242Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n"
+      "/home/lala8/miniforge3/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n"
      ]
     },
     {
@@ -6050,8 +826,8 @@
        "      <td>10.482847</td>\n",
        "      <td>0.000305</td>\n",
        "      <td>CTTCCTC</td>\n",
-       "      <td>0.026460</td>\n",
-       "      <td>0.095351</td>\n",
+       "      <td>0.026455</td>\n",
+       "      <td>0.095332</td>\n",
        "      <td>24046</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6064,8 +840,8 @@
        "      <td>10.482847</td>\n",
        "      <td>0.000305</td>\n",
        "      <td>CTTCCTC</td>\n",
-       "      <td>0.026460</td>\n",
-       "      <td>0.095351</td>\n",
+       "      <td>0.026455</td>\n",
+       "      <td>0.095332</td>\n",
        "      <td>24046</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6078,8 +854,8 @@
        "      <td>10.482847</td>\n",
        "      <td>0.000305</td>\n",
        "      <td>CTTCCTC</td>\n",
-       "      <td>0.026460</td>\n",
-       "      <td>0.095351</td>\n",
+       "      <td>0.026455</td>\n",
+       "      <td>0.095332</td>\n",
        "      <td>24046</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6092,8 +868,8 @@
        "      <td>11.578735</td>\n",
        "      <td>0.000074</td>\n",
        "      <td>CACTTCCTCC</td>\n",
-       "      <td>0.024599</td>\n",
-       "      <td>0.081912</td>\n",
+       "      <td>0.024595</td>\n",
+       "      <td>0.081899</td>\n",
        "      <td>24044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6106,8 +882,8 @@
        "      <td>13.125338</td>\n",
        "      <td>0.000025</td>\n",
        "      <td>CACTTCCTCC</td>\n",
-       "      <td>0.024599</td>\n",
-       "      <td>0.084022</td>\n",
+       "      <td>0.024595</td>\n",
+       "      <td>0.084009</td>\n",
        "      <td>24044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6120,8 +896,8 @@
        "      <td>11.578735</td>\n",
        "      <td>0.000074</td>\n",
        "      <td>CACTTCCTCC</td>\n",
-       "      <td>0.024599</td>\n",
-       "      <td>0.081912</td>\n",
+       "      <td>0.024595</td>\n",
+       "      <td>0.081899</td>\n",
        "      <td>24044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6134,8 +910,8 @@
        "      <td>13.125338</td>\n",
        "      <td>0.000025</td>\n",
        "      <td>CACTTCCTCC</td>\n",
-       "      <td>0.024599</td>\n",
-       "      <td>0.084022</td>\n",
+       "      <td>0.024595</td>\n",
+       "      <td>0.084009</td>\n",
        "      <td>24044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6148,8 +924,8 @@
        "      <td>7.401650</td>\n",
        "      <td>0.000276</td>\n",
        "      <td>CCACTTCCTCC</td>\n",
-       "      <td>0.023818</td>\n",
-       "      <td>0.067815</td>\n",
+       "      <td>0.023814</td>\n",
+       "      <td>0.067804</td>\n",
        "      <td>24043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6162,8 +938,8 @@
        "      <td>7.401650</td>\n",
        "      <td>0.000276</td>\n",
        "      <td>CCACTTCCTCC</td>\n",
-       "      <td>0.023818</td>\n",
-       "      <td>0.067815</td>\n",
+       "      <td>0.023814</td>\n",
+       "      <td>0.067804</td>\n",
        "      <td>24043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6176,8 +952,8 @@
        "      <td>9.248973</td>\n",
        "      <td>0.000253</td>\n",
        "      <td>CCACTTCCTCC</td>\n",
-       "      <td>0.023818</td>\n",
-       "      <td>0.052393</td>\n",
+       "      <td>0.023814</td>\n",
+       "      <td>0.052384</td>\n",
        "      <td>24043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6190,8 +966,8 @@
        "      <td>9.248973</td>\n",
        "      <td>0.000253</td>\n",
        "      <td>CCACTTCCTCC</td>\n",
-       "      <td>0.023818</td>\n",
-       "      <td>0.052393</td>\n",
+       "      <td>0.023814</td>\n",
+       "      <td>0.052384</td>\n",
        "      <td>24043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6204,8 +980,8 @@
        "      <td>11.822033</td>\n",
        "      <td>0.000050</td>\n",
        "      <td>CCCACTTCCTCC</td>\n",
-       "      <td>0.022757</td>\n",
-       "      <td>0.073496</td>\n",
+       "      <td>0.022753</td>\n",
+       "      <td>0.073485</td>\n",
        "      <td>24042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6218,8 +994,8 @@
        "      <td>7.946971</td>\n",
        "      <td>0.000269</td>\n",
        "      <td>CCCACTTCCTCC</td>\n",
-       "      <td>0.022757</td>\n",
-       "      <td>0.059993</td>\n",
+       "      <td>0.022753</td>\n",
+       "      <td>0.059981</td>\n",
        "      <td>24042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6232,8 +1008,8 @@
        "      <td>11.990291</td>\n",
        "      <td>0.000048</td>\n",
        "      <td>CCCACTTCCTCC</td>\n",
-       "      <td>0.022757</td>\n",
-       "      <td>0.073415</td>\n",
+       "      <td>0.022753</td>\n",
+       "      <td>0.073404</td>\n",
        "      <td>24042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6246,8 +1022,8 @@
        "      <td>7.946971</td>\n",
        "      <td>0.000269</td>\n",
        "      <td>CCCACTTCCTCC</td>\n",
-       "      <td>0.022757</td>\n",
-       "      <td>0.059993</td>\n",
+       "      <td>0.022753</td>\n",
+       "      <td>0.059981</td>\n",
        "      <td>24042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6260,8 +1036,8 @@
        "      <td>11.822033</td>\n",
        "      <td>0.000050</td>\n",
        "      <td>CCCACTTCCTCC</td>\n",
-       "      <td>0.022757</td>\n",
-       "      <td>0.073496</td>\n",
+       "      <td>0.022753</td>\n",
+       "      <td>0.073485</td>\n",
        "      <td>24042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6274,8 +1050,8 @@
        "      <td>11.990291</td>\n",
        "      <td>0.000048</td>\n",
        "      <td>CCCACTTCCTCC</td>\n",
-       "      <td>0.022757</td>\n",
-       "      <td>0.073415</td>\n",
+       "      <td>0.022753</td>\n",
+       "      <td>0.073404</td>\n",
        "      <td>24042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6288,8 +1064,8 @@
        "      <td>5.086073</td>\n",
        "      <td>0.000475</td>\n",
        "      <td>CACTTCCTCCC</td>\n",
-       "      <td>0.021733</td>\n",
-       "      <td>0.076563</td>\n",
+       "      <td>0.021729</td>\n",
+       "      <td>0.076552</td>\n",
        "      <td>24044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6302,8 +1078,8 @@
        "      <td>12.386636</td>\n",
        "      <td>0.000032</td>\n",
        "      <td>CACTTCCTCCC</td>\n",
-       "      <td>0.021733</td>\n",
-       "      <td>0.072298</td>\n",
+       "      <td>0.021729</td>\n",
+       "      <td>0.072286</td>\n",
        "      <td>24044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -6316,8 +1092,8 @@
        "      <td>12.386636</td>\n",
        "      <td>0.000032</td>\n",
        "      <td>CACTTCCTCCC</td>\n",
-       "      <td>0.021733</td>\n",
-       "      <td>0.072298</td>\n",
+       "      <td>0.021729</td>\n",
+       "      <td>0.072286</td>\n",
        "      <td>24044</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -6348,48 +1124,48 @@
        "3184      ERF.H13CORE.0.PS.A  pos.chr9_133251979_C_T_ABO-ABO@24040  74044   \n",
        "\n",
        "        end strand      score   p-value   matched_seq  site_attr_score  \\\n",
-       "994   74053      -  10.482847  0.000305       CTTCCTC         0.026460   \n",
-       "859   74053      -  10.482847  0.000305       CTTCCTC         0.026460   \n",
-       "3198  74053      -  10.482847  0.000305       CTTCCTC         0.026460   \n",
-       "3193  74054      -  11.578735  0.000074    CACTTCCTCC         0.024599   \n",
-       "857   74054      -  13.125338  0.000025    CACTTCCTCC         0.024599   \n",
-       "854   74054      -  11.578735  0.000074    CACTTCCTCC         0.024599   \n",
-       "3196  74054      -  13.125338  0.000025    CACTTCCTCC         0.024599   \n",
-       "3314  74054      -   7.401650  0.000276   CCACTTCCTCC         0.023818   \n",
-       "969   74054      -   7.401650  0.000276   CCACTTCCTCC         0.023818   \n",
-       "871   74054      -   9.248973  0.000253   CCACTTCCTCC         0.023818   \n",
-       "3213  74054      -   9.248973  0.000253   CCACTTCCTCC         0.023818   \n",
-       "3185  74054      -  11.822033  0.000050  CCCACTTCCTCC         0.022757   \n",
-       "915   74054      +   7.946971  0.000269  CCCACTTCCTCC         0.022757   \n",
-       "3199  74054      -  11.990291  0.000048  CCCACTTCCTCC         0.022757   \n",
-       "3270  74054      +   7.946971  0.000269  CCCACTTCCTCC         0.022757   \n",
-       "846   74054      -  11.822033  0.000050  CCCACTTCCTCC         0.022757   \n",
-       "860   74054      -  11.990291  0.000048  CCCACTTCCTCC         0.022757   \n",
-       "844   74055      -   5.086073  0.000475   CACTTCCTCCC         0.021733   \n",
-       "845   74055      -  12.386636  0.000032   CACTTCCTCCC         0.021733   \n",
-       "3184  74055      -  12.386636  0.000032   CACTTCCTCCC         0.021733   \n",
+       "994   74053      -  10.482847  0.000305       CTTCCTC         0.026455   \n",
+       "859   74053      -  10.482847  0.000305       CTTCCTC         0.026455   \n",
+       "3198  74053      -  10.482847  0.000305       CTTCCTC         0.026455   \n",
+       "3193  74054      -  11.578735  0.000074    CACTTCCTCC         0.024595   \n",
+       "857   74054      -  13.125338  0.000025    CACTTCCTCC         0.024595   \n",
+       "854   74054      -  11.578735  0.000074    CACTTCCTCC         0.024595   \n",
+       "3196  74054      -  13.125338  0.000025    CACTTCCTCC         0.024595   \n",
+       "3314  74054      -   7.401650  0.000276   CCACTTCCTCC         0.023814   \n",
+       "969   74054      -   7.401650  0.000276   CCACTTCCTCC         0.023814   \n",
+       "871   74054      -   9.248973  0.000253   CCACTTCCTCC         0.023814   \n",
+       "3213  74054      -   9.248973  0.000253   CCACTTCCTCC         0.023814   \n",
+       "3185  74054      -  11.822033  0.000050  CCCACTTCCTCC         0.022753   \n",
+       "915   74054      +   7.946971  0.000269  CCCACTTCCTCC         0.022753   \n",
+       "3199  74054      -  11.990291  0.000048  CCCACTTCCTCC         0.022753   \n",
+       "3270  74054      +   7.946971  0.000269  CCCACTTCCTCC         0.022753   \n",
+       "846   74054      -  11.822033  0.000050  CCCACTTCCTCC         0.022753   \n",
+       "860   74054      -  11.990291  0.000048  CCCACTTCCTCC         0.022753   \n",
+       "844   74055      -   5.086073  0.000475   CACTTCCTCCC         0.021729   \n",
+       "845   74055      -  12.386636  0.000032   CACTTCCTCCC         0.021729   \n",
+       "3184  74055      -  12.386636  0.000032   CACTTCCTCCC         0.021729   \n",
        "\n",
        "      motif_attr_score from_tss  \n",
-       "994           0.095351    24046  \n",
-       "859           0.095351    24046  \n",
-       "3198          0.095351    24046  \n",
-       "3193          0.081912    24044  \n",
-       "857           0.084022    24044  \n",
-       "854           0.081912    24044  \n",
-       "3196          0.084022    24044  \n",
-       "3314          0.067815    24043  \n",
-       "969           0.067815    24043  \n",
-       "871           0.052393    24043  \n",
-       "3213          0.052393    24043  \n",
-       "3185          0.073496    24042  \n",
-       "915           0.059993    24042  \n",
-       "3199          0.073415    24042  \n",
-       "3270          0.059993    24042  \n",
-       "846           0.073496    24042  \n",
-       "860           0.073415    24042  \n",
-       "844           0.076563    24044  \n",
-       "845           0.072298    24044  \n",
-       "3184          0.072298    24044  "
+       "994           0.095332    24046  \n",
+       "859           0.095332    24046  \n",
+       "3198          0.095332    24046  \n",
+       "3193          0.081899    24044  \n",
+       "857           0.084009    24044  \n",
+       "854           0.081899    24044  \n",
+       "3196          0.084009    24044  \n",
+       "3314          0.067804    24043  \n",
+       "969           0.067804    24043  \n",
+       "871           0.052384    24043  \n",
+       "3213          0.052384    24043  \n",
+       "3185          0.073485    24042  \n",
+       "915           0.059981    24042  \n",
+       "3199          0.073404    24042  \n",
+       "3270          0.059981    24042  \n",
+       "846           0.073485    24042  \n",
+       "860           0.073404    24042  \n",
+       "844           0.076552    24044  \n",
+       "845           0.072286    24044  \n",
+       "3184          0.072286    24044  "
       ]
      },
      "execution_count": 17,
@@ -6413,131 +1189,16 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "fcc21b89",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:15:04.651156Z",
-     "iopub.status.busy": "2025-12-20T01:15:04.651023Z",
-     "iopub.status.idle": "2025-12-20T01:15:27.476055Z",
-     "shell.execute_reply": "2025-12-20T01:15:27.475584Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:06.0 (522.3MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing recursive seqlet calling...:   0%|          | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:03<00:00,  3.32s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:03<00:00,  3.32s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'metadata:latest', 3122.32MB. 1 files...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Done. 00:00:02.0 (1546.6MB/s)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing recursive seqlet calling...:   0%|          | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/celikm5/miniforge3/envs/decima2/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:02<00:00,  2.92s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:02<00:00,  2.92s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "Computing recursive seqlet calling...:   0%|          | 0/1 [00:00<?, ?it/s]/home/lala8/miniforge3/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:03<00:00,  3.78s/it]\n",
+      "Computing recursive seqlet calling...:   0%|          | 0/1 [00:00<?, ?it/s]/home/lala8/miniforge3/envs/decima/lib/python3.11/site-packages/memelite/fimo.py:406: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "Computing recursive seqlet calling...: 100%|██████████| 1/1 [00:04<00:00,  4.11s/it]\n"
      ]
     }
    ],
@@ -6550,14 +1211,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "87537d0b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:15:27.477513Z",
-     "iopub.status.busy": "2025-12-20T01:15:27.477378Z",
-     "iopub.status.idle": "2025-12-20T01:15:27.485446Z",
-     "shell.execute_reply": "2025-12-20T01:15:27.485103Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -6605,8 +1259,8 @@
        "      <td>21.043345</td>\n",
        "      <td>2.714465e-08</td>\n",
        "      <td>TCTTTGTTTTCCCTTTTTTGTCT</td>\n",
-       "      <td>-0.003653</td>\n",
-       "      <td>-0.008740</td>\n",
+       "      <td>-0.003652</td>\n",
+       "      <td>-0.008739</td>\n",
        "      <td>2150</td>\n",
        "      <td>ref</td>\n",
        "    </tr>\n",
@@ -6620,8 +1274,8 @@
        "      <td>20.821767</td>\n",
        "      <td>3.492460e-08</td>\n",
        "      <td>GCCGGGAGGGGGCGCC</td>\n",
-       "      <td>0.016934</td>\n",
-       "      <td>0.059062</td>\n",
+       "      <td>0.016929</td>\n",
+       "      <td>0.059048</td>\n",
        "      <td>756</td>\n",
        "      <td>ref</td>\n",
        "    </tr>\n",
@@ -6635,8 +1289,8 @@
        "      <td>20.821767</td>\n",
        "      <td>3.492460e-08</td>\n",
        "      <td>GCCGGGAGGGGGCGCC</td>\n",
-       "      <td>0.016934</td>\n",
-       "      <td>0.059062</td>\n",
+       "      <td>0.016929</td>\n",
+       "      <td>0.059048</td>\n",
        "      <td>756</td>\n",
        "      <td>ref</td>\n",
        "    </tr>\n",
@@ -6650,8 +1304,8 @@
        "      <td>20.659863</td>\n",
        "      <td>3.644891e-08</td>\n",
        "      <td>AACCTGAAAAACCGGTTCA</td>\n",
-       "      <td>-0.001653</td>\n",
-       "      <td>-0.006104</td>\n",
+       "      <td>-0.001654</td>\n",
+       "      <td>-0.006107</td>\n",
        "      <td>25111</td>\n",
        "      <td>ref</td>\n",
        "    </tr>\n",
@@ -6665,8 +1319,8 @@
        "      <td>20.023842</td>\n",
        "      <td>3.720925e-08</td>\n",
        "      <td>ACCTGAAAAACCGGTTCA</td>\n",
-       "      <td>-0.001693</td>\n",
-       "      <td>-0.006160</td>\n",
+       "      <td>-0.001694</td>\n",
+       "      <td>-0.006163</td>\n",
        "      <td>25112</td>\n",
        "      <td>ref</td>\n",
        "    </tr>\n",
@@ -6686,39 +1340,39 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9765</th>\n",
+       "      <th>9586</th>\n",
        "      <td>ZN124.H13CORE.0.P.C</td>\n",
-       "      <td>pos.chr9_133251979_C_T_ABO@799</td>\n",
+       "      <td>neg.chr9_133251979_C_T_ABO@789</td>\n",
        "      <td>50797</td>\n",
        "      <td>50809</td>\n",
        "      <td>-</td>\n",
        "      <td>6.514214</td>\n",
        "      <td>4.991293e-04</td>\n",
        "      <td>CCGGGCGGAAGG</td>\n",
-       "      <td>0.030402</td>\n",
-       "      <td>0.064038</td>\n",
+       "      <td>0.030397</td>\n",
+       "      <td>0.064030</td>\n",
        "      <td>797</td>\n",
        "      <td>alt</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9766</th>\n",
-       "      <td>ZN320.H13CORE.0.P.B</td>\n",
-       "      <td>neg.chr9_133251979_C_T_ABO@24107</td>\n",
-       "      <td>74102</td>\n",
-       "      <td>74125</td>\n",
-       "      <td>+</td>\n",
-       "      <td>4.026217</td>\n",
-       "      <td>4.993357e-04</td>\n",
-       "      <td>GGGGGCGGAGTGGGGACCAGACC</td>\n",
-       "      <td>-0.001394</td>\n",
-       "      <td>-0.001536</td>\n",
-       "      <td>24102</td>\n",
+       "      <th>9587</th>\n",
+       "      <td>ZN124.H13CORE.0.P.C</td>\n",
+       "      <td>pos.chr9_133251979_C_T_ABO@799</td>\n",
+       "      <td>50797</td>\n",
+       "      <td>50809</td>\n",
+       "      <td>-</td>\n",
+       "      <td>6.514214</td>\n",
+       "      <td>4.991293e-04</td>\n",
+       "      <td>CCGGGCGGAAGG</td>\n",
+       "      <td>0.030397</td>\n",
+       "      <td>0.064030</td>\n",
+       "      <td>797</td>\n",
        "      <td>alt</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9767</th>\n",
+       "      <th>9588</th>\n",
        "      <td>ZN320.H13CORE.0.P.B</td>\n",
-       "      <td>neg.chr9_133251979_C_T_ABO@24115</td>\n",
+       "      <td>neg.chr9_133251979_C_T_ABO@24107</td>\n",
        "      <td>74102</td>\n",
        "      <td>74125</td>\n",
        "      <td>+</td>\n",
@@ -6726,12 +1380,12 @@
        "      <td>4.993357e-04</td>\n",
        "      <td>GGGGGCGGAGTGGGGACCAGACC</td>\n",
        "      <td>-0.001394</td>\n",
-       "      <td>-0.001536</td>\n",
+       "      <td>-0.001535</td>\n",
        "      <td>24102</td>\n",
        "      <td>alt</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9768</th>\n",
+       "      <th>9589</th>\n",
        "      <td>CGGBP1.H13CORE.0.PSGIB.A</td>\n",
        "      <td>neg.chr9_133251979_C_T_ABO@736</td>\n",
        "      <td>50737</td>\n",
@@ -6740,13 +1394,13 @@
        "      <td>9.182252</td>\n",
        "      <td>4.999638e-04</td>\n",
        "      <td>GGGGCGGCGGG</td>\n",
-       "      <td>-0.001905</td>\n",
+       "      <td>-0.001904</td>\n",
        "      <td>-0.002937</td>\n",
        "      <td>737</td>\n",
        "      <td>alt</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9769</th>\n",
+       "      <th>9590</th>\n",
        "      <td>CGGBP1.H13CORE.0.PSGIB.A</td>\n",
        "      <td>neg.chr9_133251979_C_T_ABO@751</td>\n",
        "      <td>50737</td>\n",
@@ -6755,14 +1409,14 @@
        "      <td>9.182252</td>\n",
        "      <td>4.999638e-04</td>\n",
        "      <td>GGGGCGGCGGG</td>\n",
-       "      <td>-0.001905</td>\n",
+       "      <td>-0.001904</td>\n",
        "      <td>-0.002937</td>\n",
        "      <td>737</td>\n",
        "      <td>alt</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>9770 rows × 12 columns</p>\n",
+       "<p>9591 rows × 12 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -6773,11 +1427,11 @@
        "3        GRHL1.H13CORE.0.PSM.A  neg.chr9_133251979_C_T_ABO@25122  75111   \n",
        "4         GRHL3.H13CORE.0.SB.A  neg.chr9_133251979_C_T_ABO@25122  75112   \n",
        "...                        ...                               ...    ...   \n",
-       "9765       ZN124.H13CORE.0.P.C    pos.chr9_133251979_C_T_ABO@799  50797   \n",
-       "9766       ZN320.H13CORE.0.P.B  neg.chr9_133251979_C_T_ABO@24107  74102   \n",
-       "9767       ZN320.H13CORE.0.P.B  neg.chr9_133251979_C_T_ABO@24115  74102   \n",
-       "9768  CGGBP1.H13CORE.0.PSGIB.A    neg.chr9_133251979_C_T_ABO@736  50737   \n",
-       "9769  CGGBP1.H13CORE.0.PSGIB.A    neg.chr9_133251979_C_T_ABO@751  50737   \n",
+       "9586       ZN124.H13CORE.0.P.C    neg.chr9_133251979_C_T_ABO@789  50797   \n",
+       "9587       ZN124.H13CORE.0.P.C    pos.chr9_133251979_C_T_ABO@799  50797   \n",
+       "9588       ZN320.H13CORE.0.P.B  neg.chr9_133251979_C_T_ABO@24107  74102   \n",
+       "9589  CGGBP1.H13CORE.0.PSGIB.A    neg.chr9_133251979_C_T_ABO@736  50737   \n",
+       "9590  CGGBP1.H13CORE.0.PSGIB.A    neg.chr9_133251979_C_T_ABO@751  50737   \n",
        "\n",
        "        end strand      score       p-value              matched_seq  \\\n",
        "0     52173      -  21.043345  2.714465e-08  TCTTTGTTTTCCCTTTTTTGTCT   \n",
@@ -6786,26 +1440,26 @@
        "3     75130      -  20.659863  3.644891e-08      AACCTGAAAAACCGGTTCA   \n",
        "4     75130      +  20.023842  3.720925e-08       ACCTGAAAAACCGGTTCA   \n",
        "...     ...    ...        ...           ...                      ...   \n",
-       "9765  50809      -   6.514214  4.991293e-04             CCGGGCGGAAGG   \n",
-       "9766  74125      +   4.026217  4.993357e-04  GGGGGCGGAGTGGGGACCAGACC   \n",
-       "9767  74125      +   4.026217  4.993357e-04  GGGGGCGGAGTGGGGACCAGACC   \n",
-       "9768  50748      -   9.182252  4.999638e-04              GGGGCGGCGGG   \n",
-       "9769  50748      -   9.182252  4.999638e-04              GGGGCGGCGGG   \n",
+       "9586  50809      -   6.514214  4.991293e-04             CCGGGCGGAAGG   \n",
+       "9587  50809      -   6.514214  4.991293e-04             CCGGGCGGAAGG   \n",
+       "9588  74125      +   4.026217  4.993357e-04  GGGGGCGGAGTGGGGACCAGACC   \n",
+       "9589  50748      -   9.182252  4.999638e-04              GGGGCGGCGGG   \n",
+       "9590  50748      -   9.182252  4.999638e-04              GGGGCGGCGGG   \n",
        "\n",
        "      site_attr_score  motif_attr_score from_tss allele  \n",
-       "0           -0.003653         -0.008740     2150    ref  \n",
-       "1            0.016934          0.059062      756    ref  \n",
-       "2            0.016934          0.059062      756    ref  \n",
-       "3           -0.001653         -0.006104    25111    ref  \n",
-       "4           -0.001693         -0.006160    25112    ref  \n",
+       "0           -0.003652         -0.008739     2150    ref  \n",
+       "1            0.016929          0.059048      756    ref  \n",
+       "2            0.016929          0.059048      756    ref  \n",
+       "3           -0.001654         -0.006107    25111    ref  \n",
+       "4           -0.001694         -0.006163    25112    ref  \n",
        "...               ...               ...      ...    ...  \n",
-       "9765         0.030402          0.064038      797    alt  \n",
-       "9766        -0.001394         -0.001536    24102    alt  \n",
-       "9767        -0.001394         -0.001536    24102    alt  \n",
-       "9768        -0.001905         -0.002937      737    alt  \n",
-       "9769        -0.001905         -0.002937      737    alt  \n",
+       "9586         0.030397          0.064030      797    alt  \n",
+       "9587         0.030397          0.064030      797    alt  \n",
+       "9588        -0.001394         -0.001535    24102    alt  \n",
+       "9589        -0.001904         -0.002937      737    alt  \n",
+       "9590        -0.001904         -0.002937      737    alt  \n",
        "\n",
-       "[9770 rows x 12 columns]"
+       "[9591 rows x 12 columns]"
       ]
      },
      "execution_count": 19,
@@ -6821,14 +1475,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "0e65e660",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-20T01:15:27.486713Z",
-     "iopub.status.busy": "2025-12-20T01:15:27.486590Z",
-     "iopub.status.idle": "2025-12-20T01:15:27.505299Z",
-     "shell.execute_reply": "2025-12-20T01:15:27.504854Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -6868,36 +1515,36 @@
        "    <tr>\n",
        "      <th>TFDP1.H13CORE.0.P.B</th>\n",
        "      <th>neg.chr9_133251979_C_T_ABO@789</th>\n",
-       "      <td>0.005604</td>\n",
-       "      <td>0.033031</td>\n",
-       "      <td>0.027427</td>\n",
+       "      <td>0.005606</td>\n",
+       "      <td>0.033026</td>\n",
+       "      <td>0.027421</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CTCFL.H13CORE.0.P.B</th>\n",
        "      <th>pos.chr9_133251979_C_T_ABO@755</th>\n",
        "      <td>-0.002148</td>\n",
-       "      <td>0.020881</td>\n",
-       "      <td>0.023029</td>\n",
+       "      <td>0.020880</td>\n",
+       "      <td>0.023028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">SP5.H13CORE.0.P.B</th>\n",
        "      <th>neg.chr9_133251979_C_T_ABO@751</th>\n",
-       "      <td>-0.002173</td>\n",
-       "      <td>0.020172</td>\n",
-       "      <td>0.022344</td>\n",
+       "      <td>-0.002172</td>\n",
+       "      <td>0.020171</td>\n",
+       "      <td>0.022343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pos.chr9_133251979_C_T_ABO@755</th>\n",
-       "      <td>-0.002173</td>\n",
-       "      <td>0.020172</td>\n",
-       "      <td>0.022344</td>\n",
+       "      <td>-0.002172</td>\n",
+       "      <td>0.020171</td>\n",
+       "      <td>0.022343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SLC2A4RG.H13CORE.0.PSG.A</th>\n",
-       "      <th>pos.chr9_133251979_C_T_ABO@799</th>\n",
-       "      <td>0.013545</td>\n",
-       "      <td>0.034428</td>\n",
-       "      <td>0.020883</td>\n",
+       "      <th>neg.chr9_133251979_C_T_ABO@789</th>\n",
+       "      <td>0.013543</td>\n",
+       "      <td>0.034423</td>\n",
+       "      <td>0.020880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -6908,86 +1555,86 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">SP5.H13CORE.0.P.B</th>\n",
-       "      <th>pos.chr9_133251979_C_T_ABO@755</th>\n",
-       "      <td>0.016457</td>\n",
-       "      <td>-0.001859</td>\n",
-       "      <td>-0.018316</td>\n",
+       "      <th>neg.chr9_133251979_C_T_ABO@751</th>\n",
+       "      <td>0.016453</td>\n",
+       "      <td>-0.001858</td>\n",
+       "      <td>-0.018311</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>neg.chr9_133251979_C_T_ABO@751</th>\n",
-       "      <td>0.016457</td>\n",
-       "      <td>-0.001859</td>\n",
-       "      <td>-0.018316</td>\n",
+       "      <th>pos.chr9_133251979_C_T_ABO@755</th>\n",
+       "      <td>0.016453</td>\n",
+       "      <td>-0.001858</td>\n",
+       "      <td>-0.018311</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CTCFL.H13CORE.0.P.B</th>\n",
        "      <th>pos.chr9_133251979_C_T_ABO@755</th>\n",
-       "      <td>0.016934</td>\n",
-       "      <td>-0.001661</td>\n",
-       "      <td>-0.018594</td>\n",
+       "      <td>0.016929</td>\n",
+       "      <td>-0.001660</td>\n",
+       "      <td>-0.018590</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">SLC2A4RG.H13CORE.0.PSG.A</th>\n",
        "      <th>pos.chr9_133251979_C_T_ABO@799</th>\n",
-       "      <td>0.036024</td>\n",
-       "      <td>0.012775</td>\n",
-       "      <td>-0.023249</td>\n",
+       "      <td>0.036018</td>\n",
+       "      <td>0.012773</td>\n",
+       "      <td>-0.023245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>neg.chr9_133251979_C_T_ABO@789</th>\n",
-       "      <td>0.036024</td>\n",
-       "      <td>0.012775</td>\n",
-       "      <td>-0.023249</td>\n",
+       "      <td>0.036018</td>\n",
+       "      <td>0.012773</td>\n",
+       "      <td>-0.023245</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>4504 rows × 3 columns</p>\n",
+       "<p>3877 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                                                         site_attr_score  \\\n",
        "motif                    peak                                              \n",
-       "TFDP1.H13CORE.0.P.B      neg.chr9_133251979_C_T_ABO@789         0.005604   \n",
+       "TFDP1.H13CORE.0.P.B      neg.chr9_133251979_C_T_ABO@789         0.005606   \n",
        "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755        -0.002148   \n",
-       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751        -0.002173   \n",
-       "                         pos.chr9_133251979_C_T_ABO@755        -0.002173   \n",
-       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799         0.013545   \n",
+       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751        -0.002172   \n",
+       "                         pos.chr9_133251979_C_T_ABO@755        -0.002172   \n",
+       "SLC2A4RG.H13CORE.0.PSG.A neg.chr9_133251979_C_T_ABO@789         0.013543   \n",
        "...                                                                  ...   \n",
-       "SP5.H13CORE.0.P.B        pos.chr9_133251979_C_T_ABO@755         0.016457   \n",
-       "                         neg.chr9_133251979_C_T_ABO@751         0.016457   \n",
-       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755         0.016934   \n",
-       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799         0.036024   \n",
-       "                         neg.chr9_133251979_C_T_ABO@789         0.036024   \n",
+       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751         0.016453   \n",
+       "                         pos.chr9_133251979_C_T_ABO@755         0.016453   \n",
+       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755         0.016929   \n",
+       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799         0.036018   \n",
+       "                         neg.chr9_133251979_C_T_ABO@789         0.036018   \n",
        "\n",
        "                                                         site_attr_score_alt  \\\n",
        "motif                    peak                                                  \n",
-       "TFDP1.H13CORE.0.P.B      neg.chr9_133251979_C_T_ABO@789             0.033031   \n",
-       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755             0.020881   \n",
-       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751             0.020172   \n",
-       "                         pos.chr9_133251979_C_T_ABO@755             0.020172   \n",
-       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799             0.034428   \n",
+       "TFDP1.H13CORE.0.P.B      neg.chr9_133251979_C_T_ABO@789             0.033026   \n",
+       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755             0.020880   \n",
+       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751             0.020171   \n",
+       "                         pos.chr9_133251979_C_T_ABO@755             0.020171   \n",
+       "SLC2A4RG.H13CORE.0.PSG.A neg.chr9_133251979_C_T_ABO@789             0.034423   \n",
        "...                                                                      ...   \n",
-       "SP5.H13CORE.0.P.B        pos.chr9_133251979_C_T_ABO@755            -0.001859   \n",
-       "                         neg.chr9_133251979_C_T_ABO@751            -0.001859   \n",
-       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755            -0.001661   \n",
-       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799             0.012775   \n",
-       "                         neg.chr9_133251979_C_T_ABO@789             0.012775   \n",
+       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751            -0.001858   \n",
+       "                         pos.chr9_133251979_C_T_ABO@755            -0.001858   \n",
+       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755            -0.001660   \n",
+       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799             0.012773   \n",
+       "                         neg.chr9_133251979_C_T_ABO@789             0.012773   \n",
        "\n",
        "                                                         site_attr_score_diff  \n",
        "motif                    peak                                                  \n",
-       "TFDP1.H13CORE.0.P.B      neg.chr9_133251979_C_T_ABO@789              0.027427  \n",
-       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755              0.023029  \n",
-       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751              0.022344  \n",
-       "                         pos.chr9_133251979_C_T_ABO@755              0.022344  \n",
-       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799              0.020883  \n",
+       "TFDP1.H13CORE.0.P.B      neg.chr9_133251979_C_T_ABO@789              0.027421  \n",
+       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755              0.023028  \n",
+       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751              0.022343  \n",
+       "                         pos.chr9_133251979_C_T_ABO@755              0.022343  \n",
+       "SLC2A4RG.H13CORE.0.PSG.A neg.chr9_133251979_C_T_ABO@789              0.020880  \n",
        "...                                                                       ...  \n",
-       "SP5.H13CORE.0.P.B        pos.chr9_133251979_C_T_ABO@755             -0.018316  \n",
-       "                         neg.chr9_133251979_C_T_ABO@751             -0.018316  \n",
-       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755             -0.018594  \n",
-       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799             -0.023249  \n",
-       "                         neg.chr9_133251979_C_T_ABO@789             -0.023249  \n",
+       "SP5.H13CORE.0.P.B        neg.chr9_133251979_C_T_ABO@751             -0.018311  \n",
+       "                         pos.chr9_133251979_C_T_ABO@755             -0.018311  \n",
+       "CTCFL.H13CORE.0.P.B      pos.chr9_133251979_C_T_ABO@755             -0.018590  \n",
+       "SLC2A4RG.H13CORE.0.PSG.A pos.chr9_133251979_C_T_ABO@799             -0.023245  \n",
+       "                         neg.chr9_133251979_C_T_ABO@789             -0.023245  \n",
        "\n",
-       "[4504 rows x 3 columns]"
+       "[3877 rows x 3 columns]"
       ]
      },
      "execution_count": 20,
@@ -7008,11 +1655,19 @@
     "df[\"site_attr_score_diff\"] = df[\"site_attr_score_alt\"] - df[\"site_attr_score\"]\n",
     "df.sort_values(\"site_attr_score_diff\", ascending=False)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91cc4e3c-887d-477d-a893-68ec50d1dc2c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "decima2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -7026,7 +1681,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,
diff --git a/setup.cfg b/setup.cfg
index 6d6101d..1d6d514 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -52,11 +52,12 @@ install_requires =
     click
     more_itertools
     wandb
+    huggingface_hub
     numpy
     torch
     pyfaidx
     genomepy
-    grelu>=1.0.10,<=1.0.11
+    grelu>=1.0.10
     lightning
     torchmetrics
     bioframe
@@ -86,9 +87,14 @@ plot =
     matplotlib
     seaborn
     plotnine
+notebooks =
+    jupyterlab
+    ipykernel
+    ipywidgets
 all =
     %(vep)s
     %(plot)s
+    %(notebooks)s
 
 # Add here test requirements (semicolon/line-separated)
 testing =
@@ -116,15 +122,16 @@ console_scripts =
 addopts =
     --cov decima --cov-report term-missing
     --verbose
+    -m "not wandb and not hf"
 norecursedirs =
     dist
     build
     .tox
 testpaths = tests
 # Use pytest markers to select/deselect specific tests
-# markers =
-#     slow: mark tests as slow (deselect with '-m "not slow"')
-#     system: mark end-to-end system tests
+markers =
+    wandb: mark tests that require wandb authentication (skipped by default)
+    hf: mark tests that call the HuggingFace API
 
 [devpi:upload]
 # Options for the devpi: PyPI server and packaging tool
diff --git a/src/decima/constants.py b/src/decima/constants.py
index af70f35..d885a35 100644
--- a/src/decima/constants.py
+++ b/src/decima/constants.py
@@ -3,6 +3,10 @@
 import json
 import os
 
+HF_MODEL_REPO = "Genentech/decima-model"
+HF_DATA_REPO = "Genentech/decima-data"
+HF_METADATA_FILENAME = "metadata.h5ad"
+
 
 # constants for all models
 DECIMA_CONTEXT_SIZE = 524_288
diff --git a/src/decima/hub/__init__.py b/src/decima/hub/__init__.py
index e50c657..de9aabe 100644
--- a/src/decima/hub/__init__.py
+++ b/src/decima/hub/__init__.py
@@ -1,32 +1,26 @@
-import os
-import json
+from pathlib import Path
 from typing import Union, Optional, List
 import warnings
-import wandb
-from pathlib import Path
-from tempfile import TemporaryDirectory
 import anndata
-from grelu.resources import get_artifact, DEFAULT_WANDB_HOST
-from decima.constants import DEFAULT_ENSEMBLE, ENSEMBLE_MODELS, MODEL_METADATA
+from huggingface_hub import hf_hub_download
+from decima.constants import (
+    DEFAULT_ENSEMBLE,
+    ENSEMBLE_MODELS,
+    MODEL_METADATA,
+    HF_MODEL_REPO,
+    HF_DATA_REPO,
+    HF_METADATA_FILENAME,
+)
 from decima.model.lightning import LightningModel, EnsembleLightningModel
 
 
-def login_wandb():
-    """Login to wandb either as anonymous or as a user."""
-    host = os.environ.get("WANDB_HOST", DEFAULT_WANDB_HOST)
-    try:
-        wandb.login(host=host, anonymous="never", timeout=0)
-    except wandb.errors.UsageError:  # login anonymously if not logged in already
-        wandb.login(host=host, relogin=True, anonymous="must", timeout=0)
-
-
 def load_decima_model(model: Union[str, int, List[str]] = DEFAULT_ENSEMBLE, device: Optional[str] = None):
-    """Load a pre-trained Decima model from wandb or local path.
+    """Load a pre-trained Decima model from HuggingFace or a local path.
 
     Args:
         model: Model identifier or path. Can be:
             - int: Replicate number (0-3)
-            - str: Model name on wandb
+            - str: Model name from MODEL_METADATA
             - str: Path to local model checkpoint
             - List: list of local model checkpoints
         device: Device to load the model on. If None, automatically selects the best available device.
@@ -40,56 +34,46 @@ def load_decima_model(model: Union[str, int, List[str]] = DEFAULT_ENSEMBLE, devi
     if isinstance(model, LightningModel):
         return model
 
-    elif model in ENSEMBLE_MODELS:
+    if model in ENSEMBLE_MODELS:
         return EnsembleLightningModel(
             [load_decima_model(model_name, device) for model_name in MODEL_METADATA[model]],
             name=model,
         )
 
-    elif isinstance(model, List):
+    if isinstance(model, list):
         if len(model) == 1:
             return load_decima_model(model[0], device)
-        else:
-            return EnsembleLightningModel([load_decima_model(path, device) for path in model], name=model)
+        return EnsembleLightningModel([load_decima_model(path, device) for path in model], name=model)
+
+    if isinstance(model, str) and Path(model).exists():
+        if model.endswith(".ckpt"):
+            return LightningModel.load_from_checkpoint(model, map_location=device)
+        return LightningModel.load_safetensor(model, device=device)
 
-    # Load directly from a path
     if model in MODEL_METADATA:
-        model_name = MODEL_METADATA[model]["name"]
-        if "model_path" in MODEL_METADATA[model]:  # if model path exist in metadata load it from the path
+        if "model_path" in MODEL_METADATA[model] and Path(MODEL_METADATA[model]["model_path"]).exists():
             return load_decima_model(MODEL_METADATA[model]["model_path"], device)
-    elif isinstance(model, str) and Path(model).exists():
-        if model.endswith("ckpt"):
-            return LightningModel.load_from_checkpoint(model, map_location=device)
-        else:
-            return LightningModel.load_safetensor(model, device=device)
-    else:
-        raise ValueError(
-            f"Invalid model: {model} it needs to be either a string of model_names on wandb ("
-            f"{list(MODEL_METADATA.keys())}), path to a local model, or a list of paths."
-        )
-    # load model from wandb
-    art = get_artifact(
-        model_name,
-        project="decima",
-        host=os.environ.get("WANDB_HOST", DEFAULT_WANDB_HOST),
+        name = MODEL_METADATA[model]["name"]
+        cached = hf_hub_download(repo_id=HF_MODEL_REPO, filename=f"{name}.safetensors")
+        return LightningModel.load_safetensor(cached, device=device)
+
+    raise ValueError(
+        f"Invalid model: {model}. Must be a known model name {list(MODEL_METADATA.keys())}, "
+        "a local path, or a list of local paths."
     )
-    with TemporaryDirectory() as d:
-        art.download(d)
-        return LightningModel.load_safetensor(Path(d) / f"{model_name}.safetensors", device=device)
 
 
 def load_decima_metadata(name_or_path: Optional[str] = None):
-    """Load the Decima metadata from wandb.
+    """Load the Decima metadata from HuggingFace or a local path.
 
     Args:
-        name_or_path: Path to local metadata file or name of the model to load metadata for using wandb. If None, default model's metadata will be downloaded from wandb.
+        name_or_path: Path to local metadata file or model name. If None, downloads from HuggingFace.
 
     Returns:
         An AnnData object containing the Decima metadata.
     """
-    if name_or_path is not None:
-        if Path(name_or_path).exists():
-            return anndata.read_h5ad(name_or_path)
+    if name_or_path is not None and Path(name_or_path).exists():
+        return anndata.read_h5ad(name_or_path)
 
     name_or_path = name_or_path or DEFAULT_ENSEMBLE
 
@@ -98,22 +82,17 @@ def load_decima_metadata(name_or_path: Optional[str] = None):
 
     if name_or_path in MODEL_METADATA:
         metadata = MODEL_METADATA[name_or_path]
-
-    if "metadata_path" in metadata:
-        if Path(metadata["metadata_path"]).exists():
-            return anndata.read_h5ad(metadata["metadata_path"])
-        else:
+        if "metadata_path" in metadata:
+            if Path(metadata["metadata_path"]).exists():
+                return anndata.read_h5ad(metadata["metadata_path"])
             warnings.warn(
-                f"Metadata `{metadata['metadata_path']}` provided in environment variables, "
-                f"but not found in `{metadata['metadata_path']}` "
-                f"Trying to download `metadata` from wandb."
+                f"Metadata path `{metadata['metadata_path']}` not found. Downloading from HuggingFace."
             )
+    else:
+        raise ValueError(
+            f"Invalid metadata: {name_or_path}. Must be a known model name {list(MODEL_METADATA.keys())} "
+            "or a local path."
+        )
 
-    art = get_artifact(
-        metadata["metadata"],
-        project="decima",
-        host=os.environ.get("WANDB_HOST", DEFAULT_WANDB_HOST),
-    )
-    with TemporaryDirectory() as d:
-        art.download(d)
-        return anndata.read_h5ad(Path(d) / f"{metadata['metadata']}.h5ad")
+    cached = hf_hub_download(repo_id=HF_DATA_REPO, filename=HF_METADATA_FILENAME, repo_type="dataset")
+    return anndata.read_h5ad(cached)
diff --git a/src/decima/hub/download.py b/src/decima/hub/download.py
index 6788e74..e3d9a54 100644
--- a/src/decima/hub/download.py
+++ b/src/decima/hub/download.py
@@ -1,12 +1,17 @@
-import os
+import logging
 from pathlib import Path
 from typing import Union
-import logging
 import genomepy
-from grelu.resources import get_artifact, DEFAULT_WANDB_HOST
-from decima.constants import DEFAULT_ENSEMBLE, ENSEMBLE_MODELS, MODEL_METADATA
-from decima.hub import login_wandb, load_decima_model, load_decima_metadata
-
+from huggingface_hub import hf_hub_download
+from decima.constants import (
+    DEFAULT_ENSEMBLE,
+    ENSEMBLE_MODELS,
+    MODEL_METADATA,
+    HF_MODEL_REPO,
+    HF_DATA_REPO,
+    HF_METADATA_FILENAME,
+)
+from decima.hub import load_decima_model, load_decima_metadata
 
 logger = logging.getLogger("decima")
 
@@ -18,86 +23,75 @@ def cache_hg38():
 
 
 def cache_decima_weights():
-    """Download pre-trained Decima model weights from wandb."""
+    """Download pre-trained Decima model weights from HuggingFace."""
     logger.info("Downloading Decima model weights...")
     for rep in MODEL_METADATA[DEFAULT_ENSEMBLE]:
         load_decima_model(rep)
 
 
 def cache_decima_metadata():
-    """Download pre-trained Decima model data from wandb."""
+    """Download pre-trained Decima metadata from HuggingFace."""
     logger.info("Downloading Decima metadata...")
     load_decima_metadata()
 
 
 def cache_decima_data():
     """Download all required data for Decima."""
-    login_wandb()
     cache_hg38()
     cache_decima_weights()
     cache_decima_metadata()
 
 
 def download_decima_weights(model: Union[str, int] = DEFAULT_ENSEMBLE, download_dir: str = "."):
-    """Download pre-trained Decima model weights from wandb.
+    """Download pre-trained Decima model weights from HuggingFace to a local directory.
 
     Args:
-        model_name: Model name or replicate number.
-        download_dir: Directory to download the model weights.
+        model: Model name or replicate number.
+        download_dir: Directory to save the model weights.
 
     Returns:
         Path to the downloaded model weights.
     """
     if model in ENSEMBLE_MODELS:
-        return [download_decima_weights(model, download_dir) for model in MODEL_METADATA[model]]
+        return [download_decima_weights(m, download_dir) for m in MODEL_METADATA[model]]
 
-    model_name = MODEL_METADATA[model]["name"]
+    name = MODEL_METADATA[model]["name"]
     download_dir = Path(download_dir)
     download_dir.mkdir(parents=True, exist_ok=True)
-    logger.info(f"Downloading Decima model weights for {model} to {download_dir / f'{model_name}.safetensors'}")
-
-    art = get_artifact(model_name, project="decima", host=os.environ.get("WANDB_HOST", DEFAULT_WANDB_HOST))
-    art.download(str(download_dir))
-    return download_dir / f"{model_name}.safetensors"
+    logger.info(f"Downloading Decima model weights for {model} to {download_dir / f'{name}.safetensors'}")
+    return Path(hf_hub_download(repo_id=HF_MODEL_REPO, filename=f"{name}.safetensors", local_dir=str(download_dir)))
 
 
-def download_decima_metadata(metadata: str = DEFAULT_ENSEMBLE, download_dir: str = "."):
-    """Download pre-trained Decima model data from wandb.
+def download_decima_metadata(download_dir: str = "."):
+    """Download pre-trained Decima metadata from HuggingFace to a local directory.
 
     Args:
-        download_dir: Directory to download the metadata.
-        metadata: Name of the model to download metadata for using wandb.
+        download_dir: Directory to save the metadata.
 
     Returns:
         Path to the downloaded metadata.
     """
-    metadata = metadata or DEFAULT_ENSEMBLE
-    if metadata in ENSEMBLE_MODELS:
-        metadata = MODEL_METADATA[metadata][0]
-
-    metadata_name = MODEL_METADATA[metadata]["metadata"]
-    art = get_artifact(metadata_name, project="decima", host=os.environ.get("WANDB_HOST", DEFAULT_WANDB_HOST))
     download_dir = Path(download_dir)
     download_dir.mkdir(parents=True, exist_ok=True)
-    logger.info(f"Downloading Decima metadata to {download_dir / f'{metadata_name}.h5ad'}.")
-
-    art.download(str(download_dir))
-    return download_dir / f"{metadata_name}.h5ad"
+    logger.info(f"Downloading Decima metadata to {download_dir / HF_METADATA_FILENAME}")
+    return Path(hf_hub_download(
+        repo_id=HF_DATA_REPO, filename=HF_METADATA_FILENAME, repo_type="dataset", local_dir=str(download_dir)
+    ))
 
 
 def download_decima(model: str = DEFAULT_ENSEMBLE, download_dir: str = "."):
     """Download all required data for Decima.
 
     Args:
-        download_dir: Directory to download the model weights and metadata.
+        model: Model name or replicate number.
+        download_dir: Directory to save the model weights and metadata.
 
     Returns:
-        Path to the downloaded directory containing the model weights and metadata.
+        Path to the download directory.
     """
     download_dir = Path(download_dir)
     download_dir.mkdir(parents=True, exist_ok=True)
     logger.info(f"Downloading Decima model weights and metadata to {download_dir}:")
-
     download_decima_weights(model, download_dir)
-    download_decima_metadata(model, download_dir)
+    download_decima_metadata(download_dir)
     return download_dir
diff --git a/src/decima/hub/wandb.py b/src/decima/hub/wandb.py
new file mode 100644
index 0000000..9c0196a
--- /dev/null
+++ b/src/decima/hub/wandb.py
@@ -0,0 +1,142 @@
+"""Wandb-based model loading for internal/private model access."""
+
+import os
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Union, Optional, List
+import warnings
+import wandb
+import anndata
+from decima.constants import DEFAULT_ENSEMBLE, ENSEMBLE_MODELS, MODEL_METADATA
+from decima.model.lightning import LightningModel, EnsembleLightningModel
+
+WANDB_ENTITY = "grelu"
+WANDB_PROJECT = "decima"
+DEFAULT_WANDB_HOST = "https://wandb.ai"
+
+
+def _get_host(host: Optional[str] = None) -> str:
+    return host or os.environ.get("WANDB_HOST", DEFAULT_WANDB_HOST)
+
+
+def login_wandb(host: Optional[str] = None):
+    """Login to wandb either as anonymous or as a user."""
+    h = _get_host(host)
+    try:
+        wandb.login(host=h, anonymous="never", timeout=0)
+    except wandb.errors.UsageError:
+        wandb.login(host=h, relogin=True, anonymous="must", timeout=0)
+
+
+def _get_artifact(name: str, host: Optional[str] = None):
+    api = wandb.Api(overrides={"host": _get_host(host)})
+    return api.artifact(f"{WANDB_ENTITY}/{WANDB_PROJECT}/{name}:latest")
+
+
+def load_decima_model(
+    model: Union[str, int, List[str]] = DEFAULT_ENSEMBLE,
+    device: Optional[str] = None,
+    host: Optional[str] = None,
+):
+    """Load a pre-trained Decima model from wandb or a local path."""
+    if isinstance(model, LightningModel):
+        return model
+
+    if model in ENSEMBLE_MODELS:
+        return EnsembleLightningModel(
+            [load_decima_model(m, device, host) for m in MODEL_METADATA[model]],
+            name=model,
+        )
+
+    if isinstance(model, list):
+        if len(model) == 1:
+            return load_decima_model(model[0], device, host)
+        return EnsembleLightningModel([load_decima_model(p, device, host) for p in model], name=model)
+
+    if isinstance(model, str) and Path(model).exists():
+        if model.endswith(".ckpt"):
+            return LightningModel.load_from_checkpoint(model, map_location=device)
+        return LightningModel.load_safetensor(model, device=device)
+
+    if model in MODEL_METADATA:
+        if "model_path" in MODEL_METADATA[model]:
+            return load_decima_model(MODEL_METADATA[model]["model_path"], device, host)
+        name = MODEL_METADATA[model]["name"]
+        art = _get_artifact(name, host)
+        with TemporaryDirectory() as d:
+            art.download(d)
+            return LightningModel.load_safetensor(Path(d) / f"{name}.safetensors", device=device)
+
+    raise ValueError(
+        f"Invalid model: {model}. Must be a known model name {list(MODEL_METADATA.keys())}, "
+        "a local path, or a list of local paths."
+    )
+
+
+def load_decima_metadata(name_or_path: Optional[str] = None, host: Optional[str] = None):
+    """Load the Decima metadata from wandb or a local path."""
+    if name_or_path is not None and Path(name_or_path).exists():
+        return anndata.read_h5ad(name_or_path)
+
+    name_or_path = name_or_path or DEFAULT_ENSEMBLE
+
+    if name_or_path in ENSEMBLE_MODELS:
+        name_or_path = MODEL_METADATA[name_or_path][0]
+
+    if name_or_path in MODEL_METADATA:
+        metadata = MODEL_METADATA[name_or_path]
+        if "metadata_path" in metadata:
+            if Path(metadata["metadata_path"]).exists():
+                return anndata.read_h5ad(metadata["metadata_path"])
+            warnings.warn(
+                f"Metadata path `{metadata['metadata_path']}` not found. Downloading from wandb."
+            )
+        art = _get_artifact(metadata["metadata"], host)
+        with TemporaryDirectory() as d:
+            art.download(d)
+            return anndata.read_h5ad(Path(d) / f"{metadata['metadata']}.h5ad")
+
+    raise ValueError(f"Unknown model: {name_or_path}")
+
+
+def download_decima_weights(
+    model: Union[str, int] = DEFAULT_ENSEMBLE,
+    download_dir: str = ".",
+    host: Optional[str] = None,
+):
+    """Download pre-trained Decima model weights from wandb."""
+    if model in ENSEMBLE_MODELS:
+        return [download_decima_weights(m, download_dir, host) for m in MODEL_METADATA[model]]
+
+    name = MODEL_METADATA[model]["name"]
+    download_dir = Path(download_dir)
+    download_dir.mkdir(parents=True, exist_ok=True)
+    art = _get_artifact(name, host)
+    art.download(str(download_dir))
+    return download_dir / f"{name}.safetensors"
+
+
+def download_decima_metadata(
+    metadata: str = DEFAULT_ENSEMBLE,
+    download_dir: str = ".",
+    host: Optional[str] = None,
+):
+    """Download pre-trained Decima metadata from wandb."""
+    metadata = metadata or DEFAULT_ENSEMBLE
+    if metadata in ENSEMBLE_MODELS:
+        metadata = MODEL_METADATA[metadata][0]
+
+    metadata_name = MODEL_METADATA[metadata]["metadata"]
+    download_dir = Path(download_dir)
+    download_dir.mkdir(parents=True, exist_ok=True)
+    art = _get_artifact(metadata_name, host)
+    art.download(str(download_dir))
+    return download_dir / f"{metadata_name}.h5ad"
+
+
+def cache_decima_data(host: Optional[str] = None):
+    """Download all required Decima assets from wandb."""
+    login_wandb(host)
+    for rep in MODEL_METADATA[DEFAULT_ENSEMBLE]:
+        load_decima_model(rep, host=host)
+    load_decima_metadata(host=host)
diff --git a/tests/conftest.py b/tests/conftest.py
index 82d5336..97ad5e5 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -6,7 +6,6 @@
 import pytest
 
 from decima.constants import DECIMA_CONTEXT_SIZE
-from decima.hub import login_wandb
 from decima.hub.download import cache_hg38
 
 
@@ -38,7 +37,6 @@ def pytest_collection_modifyitems(config, items):
                 item.add_marker(skip_long_running)
 
 
-login_wandb()
 cache_hg38()
 
 
diff --git a/tests/test_hub.py b/tests/test_hub.py
index 0bcda93..0063c6c 100644
--- a/tests/test_hub.py
+++ b/tests/test_hub.py
@@ -2,9 +2,16 @@
 import anndata
 from decima.model.lightning import LightningModel
 from decima.hub import load_decima_model, load_decima_metadata
+from decima.constants import HF_MODEL_REPO, HF_DATA_REPO, HF_METADATA_FILENAME
 
 
-@pytest.mark.long_running
+def test_hf_constants():
+    assert HF_MODEL_REPO == "Genentech/decima-model"
+    assert HF_DATA_REPO == "Genentech/decima-data"
+    assert HF_METADATA_FILENAME == "metadata.h5ad"
+
+
+@pytest.mark.hf
 def test_load_decima_model():
     model_0 = load_decima_model()
     assert model_0 is not None
@@ -14,6 +21,7 @@ def test_load_decima_model():
     assert model_2 is not None
 
 
+@pytest.mark.hf
 def test_load_decima_metadata():
     metadata = load_decima_metadata()
     assert isinstance(metadata, anndata.AnnData)