black reformatting of two notebooks

Javier Rodriguez Zaurin · Javier Rodriguez Zaurin · commit 7e5d118ec8a6 · 2022-10-07T11:25:17.000+02:00
diff --git a/examples/notebooks/10_3rd_party_integration-RayTune_WnB.ipynb b/examples/notebooks/10_3rd_party_integration-RayTune_WnB.ipynb
@@ -143,6 +143,7 @@
     "        quantity.\n",
     "\n",
     "    \"\"\"\n",
+    "\n",
     "    def __init__(\n",
     "        self,\n",
     "        wb: object,\n",
@@ -1061,7 +1062,7 @@
     "    \"wandb\": {\n",
     "        \"project\": \"test\",\n",
     "        # \"api_key_file\": os.getcwd() + \"/wandb_api.key\",\n",
-    "        \"api_key\": \"WNB_API_KEY\", \n",
+    "        \"api_key\": \"WNB_API_KEY\",\n",
     "    },\n",
     "}\n",
     "\n",
@@ -1080,7 +1081,12 @@
     "    trainer = Trainer(\n",
     "        model,\n",
     "        objective=\"binary_focal_loss\",\n",
-    "        callbacks=[RayTuneReporter, WnBReportBest(wb=wandb), early_stopping, model_checkpoint],\n",
+    "        callbacks=[\n",
+    "            RayTuneReporter,\n",
+    "            WnBReportBest(wb=wandb),\n",
+    "            early_stopping,\n",
+    "            model_checkpoint,\n",
+    "        ],\n",
     "        lr_schedulers={\"deeptabular\": deep_sch},\n",
     "        initializers={\"deeptabular\": XavierNormal},\n",
     "        optimizers={\"deeptabular\": deep_opt},\n",
diff --git a/mkdocs/sources/examples/10_3rd_party_integration-RayTune_WnB.ipynb b/mkdocs/sources/examples/10_3rd_party_integration-RayTune_WnB.ipynb
@@ -143,6 +143,7 @@
     "        quantity.\n",
     "\n",
     "    \"\"\"\n",
+    "\n",
     "    def __init__(\n",
     "        self,\n",
     "        wb: object,\n",
@@ -1061,7 +1062,7 @@
     "    \"wandb\": {\n",
     "        \"project\": \"test\",\n",
     "        # \"api_key_file\": os.getcwd() + \"/wandb_api.key\",\n",
-    "        \"api_key\": \"WNB_API_KEY\", \n",
+    "        \"api_key\": \"WNB_API_KEY\",\n",
     "    },\n",
     "}\n",
     "\n",
@@ -1080,7 +1081,12 @@
     "    trainer = Trainer(\n",
     "        model,\n",
     "        objective=\"binary_focal_loss\",\n",
-    "        callbacks=[RayTuneReporter, WnBReportBest(wb=wandb), early_stopping, model_checkpoint],\n",
+    "        callbacks=[\n",
+    "            RayTuneReporter,\n",
+    "            WnBReportBest(wb=wandb),\n",
+    "            early_stopping,\n",
+    "            model_checkpoint,\n",
+    "        ],\n",
     "        lr_schedulers={\"deeptabular\": deep_sch},\n",
     "        initializers={\"deeptabular\": XavierNormal},\n",
     "        optimizers={\"deeptabular\": deep_opt},\n",
