diff --git a/docs/tutorials/scgen_perturbation_prediction.ipynb b/docs/tutorials/scgen_perturbation_prediction.ipynb
index 7fc94c6..bc456fd 100644
--- a/docs/tutorials/scgen_perturbation_prediction.ipynb
+++ b/docs/tutorials/scgen_perturbation_prediction.ipynb
@@ -14,7 +14,8 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "#if branch is stable, will install via pypi, else will install from source\n",
+    "\n",
+    "# if branch is stable, will install via pypi, else will install from source\n",
     "branch = \"stable\"\n",
     "IN_COLAB = \"google.colab\" in sys.modules\n",
     "\n",
@@ -78,8 +79,10 @@
     }
    ],
    "source": [
-    "train = sc.read(\"./tests/data/train_kang.h5ad\",\n",
-    "                backup_url='https://drive.google.com/uc?id=1r87vhoLLq6PXAYdmyyd89zG90eJOFYLk')"
+    "train = sc.read(\n",
+    "    \"./tests/data/train_kang.h5ad\",\n",
+    "    backup_url=\"https://drive.google.com/uc?id=1r87vhoLLq6PXAYdmyyd89zG90eJOFYLk\",\n",
+    ")"
    ]
   },
   {
@@ -95,8 +98,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_new = train[~((train.obs[\"cell_type\"] == \"CD4T\") &\n",
-    "                    (train.obs[\"condition\"] == \"stimulated\"))].copy()"
+    "train_new = train[\n",
+    "    ~((train.obs[\"cell_type\"] == \"CD4T\") & (train.obs[\"condition\"] == \"stimulated\"))\n",
+    "].copy()"
    ]
   },
   {
@@ -198,10 +202,7 @@
    ],
    "source": [
     "model.train(\n",
-    "    max_epochs=100,\n",
-    "    batch_size=32,\n",
-    "    early_stopping=True,\n",
-    "    early_stopping_patience=25\n",
+    "    max_epochs=100, batch_size=32, early_stopping=True, early_stopping_patience=25\n",
     ")"
    ]
   },
@@ -258,8 +259,13 @@
    "source": [
     "sc.pp.neighbors(latent_adata)\n",
     "sc.tl.umap(latent_adata)\n",
-    "sc.pl.umap(latent_adata, color=['condition', 'cell_type'], wspace=0.4, frameon=False,\n",
-    "           save='latentspace_batch32_klw000005_z100__100e.pdf')"
+    "sc.pl.umap(\n",
+    "    latent_adata,\n",
+    "    color=[\"condition\", \"cell_type\"],\n",
+    "    wspace=0.4,\n",
+    "    frameon=False,\n",
+    "    save=\"latentspace_batch32_klw000005_z100__100e.pdf\",\n",
+    ")"
    ]
   },
   {
@@ -339,11 +345,9 @@
    ],
    "source": [
     "pred, delta = model.predict(\n",
-    "    ctrl_key='control',\n",
-    "    stim_key='stimulated',\n",
-    "    celltype_to_predict='CD4T'\n",
+    "    ctrl_key=\"control\", stim_key=\"stimulated\", celltype_to_predict=\"CD4T\"\n",
     ")\n",
-    "pred.obs['condition'] = 'pred'"
+    "pred.obs[\"condition\"] = \"pred\""
    ]
   },
   {
@@ -380,8 +384,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ctrl_adata = train[((train.obs['cell_type'] == 'CD4T') & (train.obs['condition'] == 'control'))]\n",
-    "stim_adata = train[((train.obs['cell_type'] == 'CD4T') & (train.obs['condition'] == 'stimulated'))]"
+    "ctrl_adata = train[\n",
+    "    ((train.obs[\"cell_type\"] == \"CD4T\") & (train.obs[\"condition\"] == \"control\"))\n",
+    "]\n",
+    "stim_adata = train[\n",
+    "    ((train.obs[\"cell_type\"] == \"CD4T\") & (train.obs[\"condition\"] == \"stimulated\"))\n",
+    "]"
    ]
   },
   {
@@ -433,8 +441,12 @@
    ],
    "source": [
     "sc.tl.pca(eval_adata)\n",
-    "sc.pl.pca(eval_adata, color=\"condition\", frameon=False,\n",
-    "           save='pred_stim_b32_klw000005_z100__100e.pdf')"
+    "sc.pl.pca(\n",
+    "    eval_adata,\n",
+    "    color=\"condition\",\n",
+    "    frameon=False,\n",
+    "    save=\"pred_stim_b32_klw000005_z100__100e.pdf\",\n",
+    ")"
    ]
   },
   {
@@ -466,7 +478,7 @@
     }
    ],
    "source": [
-    "CD4T = train[train.obs[\"cell_type\"] ==\"CD4T\"]"
+    "CD4T = train[train.obs[\"cell_type\"] == \"CD4T\"]"
    ]
   },
   {
@@ -527,7 +539,7 @@
     "    labels={\"x\": \"predicted\", \"y\": \"ground truth\"},\n",
     "    path_to_save=\"./reg_mean1.pdf\",\n",
     "    show=True,\n",
-    "    legend=False\n",
+    "    legend=False,\n",
     ")"
    ]
   },
@@ -567,11 +579,11 @@
     "    eval_adata,\n",
     "    axis_keys={\"x\": \"pred\", \"y\": \"stimulated\"},\n",
     "    gene_list=diff_genes[:10],\n",
-    "    top_100_genes= diff_genes,\n",
-    "    labels={\"x\": \"predicted\",\"y\": \"ground truth\"},\n",
+    "    top_100_genes=diff_genes,\n",
+    "    labels={\"x\": \"predicted\", \"y\": \"ground truth\"},\n",
     "    path_to_save=\"./reg_mean1.pdf\",\n",
     "    show=True,\n",
-    "    legend=False\n",
+    "    legend=False,\n",
     ")"
    ]
   },