diff --git a/examples/0_quick_start_guide.ipynb b/examples/0_quick_start_guide.ipynb
index 871b0f6..0f8daa0 100644
--- a/examples/0_quick_start_guide.ipynb
+++ b/examples/0_quick_start_guide.ipynb
@@ -99,7 +99,9 @@
    "source": [
     "from spektral.data import DisjointLoader\n",
     "\n",
-    "train, test, val = dataset.split_test_train_validation(split_train=4, split_test=1, split_validation=1, random_seed=42)"
+    "train, test, val = dataset.split_test_train_validation(\n",
+    "    split_train=4, split_test=1, split_validation=1, random_seed=42\n",
+    ")"
    ]
   },
   {
@@ -130,19 +132,12 @@
     "\n",
     "from tensorflow.keras.losses import BinaryCrossentropy\n",
     "from tensorflow.keras.optimizers import Adam\n",
-    "from tensorflow.keras.metrics import (\n",
-    "    AUC, BinaryAccuracy\n",
-    ")\n",
+    "from tensorflow.keras.metrics import AUC, BinaryAccuracy\n",
     "\n",
     "model = CrystalGraphClassifier()\n",
     "\n",
     "model.compile(\n",
-    "    loss=BinaryCrossentropy(), \n",
-    "    optimizer=Adam(),\n",
-    "    metrics=[\n",
-    "        AUC(), \n",
-    "        BinaryAccuracy()\n",
-    "    ]\n",
+    "    loss=BinaryCrossentropy(), optimizer=Adam(), metrics=[AUC(), BinaryAccuracy()]\n",
     ")"
    ]
   },
@@ -236,14 +231,12 @@
     "loader_va = DisjointLoader(val, epochs=1, shuffle=False, batch_size=batch_size)\n",
     "\n",
     "model.fit(\n",
-    "    loader_tr.load(), \n",
+    "    loader_tr.load(),\n",
     "    epochs=epochs,\n",
-    "    steps_per_epoch=loader_tr.steps_per_epoch, \n",
+    "    steps_per_epoch=loader_tr.steps_per_epoch,\n",
     "    use_multiprocessing=True,\n",
     "    validation_data=loader_va.load(),\n",
-    "    callbacks=[\n",
-    "        EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
-    "    ]\n",
+    "    callbacks=[EarlyStopping(monitor=\"loss\", patience=5, restore_best_weights=True)],\n",
     ")"
    ]
   },
@@ -390,7 +383,6 @@
     }
    ],
    "source": [
-    "\n",
     "loader_te = DisjointLoader(test, batch_size=batch_size, epochs=1, shuffle=False)\n",
     "loaded_pred = model.predict(loader_te.load(), use_multiprocessing=True)\n",
     "\n",
diff --git a/examples/1_kloppy_gnn_train.ipynb b/examples/1_kloppy_gnn_train.ipynb
index 96a69ad..9576db0 100644
--- a/examples/1_kloppy_gnn_train.ipynb
+++ b/examples/1_kloppy_gnn_train.ipynb
@@ -251,8 +251,7 @@
     "\n",
     "for match_id in match_ids:\n",
     "    match_pickle_file_path = compressed_pickle_file_path.format(\n",
-    "        pickle_folder=pickle_folder,\n",
-    "        match_id=match_id\n",
+    "        pickle_folder=pickle_folder, match_id=match_id\n",
     "    )\n",
     "    # if the output file already exists, skip this whole step\n",
     "    if not exists(match_pickle_file_path):\n",
@@ -551,18 +550,19 @@
     "class CrystalGraphClassifier(Model):\n",
     "    def __init__(\n",
     "        self,\n",
-    "        n_layers: int = 3, \n",
-    "        channels: int = 128, \n",
+    "        n_layers: int = 3,\n",
+    "        channels: int = 128,\n",
     "        drop_out: float = 0.5,\n",
-    "        n_out: int = 1, \n",
-    "        **kwargs):\n",
+    "        n_out: int = 1,\n",
+    "        **kwargs\n",
+    "    ):\n",
     "        super().__init__(**kwargs)\n",
-    "        \n",
+    "\n",
     "        self.n_layers = n_layers\n",
     "        self.channels = channels\n",
     "        self.drop_out = drop_out\n",
     "        self.n_out = n_out\n",
-    "        \n",
+    "\n",
     "        self.conv1 = CrystalConv()\n",
     "        self.convs = [CrystalConv() for _ in range(1, self.n_layers)]\n",
     "        self.pool = GlobalAvgPool()\n",
@@ -571,7 +571,7 @@
     "        self.dense2 = Dense(self.channels, activation=\"relu\")\n",
     "        self.dense3 = Dense(self.n_out, activation=\"sigmoid\")\n",
     "\n",
-    "    def call(self, inputs):            \n",
+    "    def call(self, inputs):\n",
     "        x, a, e, i = inputs\n",
     "        x = self.conv1([x, a, e])\n",
     "        for conv in self.convs:\n",
@@ -601,7 +601,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "from spektral.data import DisjointLoader\n",
     "\n",
     "loader_tr = DisjointLoader(train, batch_size=batch_size, epochs=epochs)\n",
@@ -642,9 +641,7 @@
     }
    ],
    "source": [
-    "from tensorflow.keras.metrics import (\n",
-    "    AUC, BinaryAccuracy\n",
-    ")\n",
+    "from tensorflow.keras.metrics import AUC, BinaryAccuracy\n",
     "from tensorflow.keras.losses import BinaryCrossentropy\n",
     "from tensorflow.keras.optimizers import Adam\n",
     "from tensorflow.keras.callbacks import EarlyStopping\n",
@@ -652,13 +649,8 @@
     "model = CrystalGraphClassifier()\n",
     "\n",
     "model.compile(\n",
-    "    loss=BinaryCrossentropy(), \n",
-    "    optimizer=Adam(),\n",
-    "    metrics=[\n",
-    "        AUC(), \n",
-    "        BinaryAccuracy()\n",
-    "    ]\n",
-    ")\n"
+    "    loss=BinaryCrossentropy(), optimizer=Adam(), metrics=[AUC(), BinaryAccuracy()]\n",
+    ")"
    ]
   },
   {
@@ -750,14 +742,12 @@
    ],
    "source": [
     "model.fit(\n",
-    "    loader_tr.load(), \n",
-    "    steps_per_epoch=loader_tr.steps_per_epoch, \n",
+    "    loader_tr.load(),\n",
+    "    steps_per_epoch=loader_tr.steps_per_epoch,\n",
     "    epochs=10,\n",
     "    use_multiprocessing=True,\n",
     "    validation_data=loader_va.load(),\n",
-    "    callbacks=[\n",
-    "        EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
-    "    ]\n",
+    "    callbacks=[EarlyStopping(monitor=\"loss\", patience=5, restore_best_weights=True)],\n",
     ")"
    ]
   },
@@ -797,7 +787,7 @@
    "source": [
     "from tensorflow.keras.models import load_model\n",
     "\n",
-    "model_path = 'models/my-first-graph-classifier'\n",
+    "model_path = \"models/my-first-graph-classifier\"\n",
     "model.save(model_path)\n",
     "loaded_model = load_model(model_path)"
    ]
@@ -852,13 +842,12 @@
     "kloppy_dataset = skillcorner.load_open_data(\n",
     "    match_id=2068,\n",
     "    include_empty_frames=False,\n",
-    "    limit=500,  \n",
+    "    limit=500,\n",
     ")\n",
     "\n",
     "preds_converter = GraphConverter(\n",
-    "    dataset=kloppy_dataset, \n",
+    "    dataset=kloppy_dataset,\n",
     "    prediction=True,\n",
-    "    \n",
     "    ball_carrier_treshold=25.0,\n",
     "    max_player_speed=12.0,\n",
     "    max_ball_speed=28.0,\n",
@@ -908,7 +897,9 @@
     "# Compute the graphs and add them to the CustomSpektralDataset\n",
     "pred_dataset = CustomSpektralDataset(graphs=preds_converter.to_spektral_graphs())\n",
     "\n",
-    "loader_pred = DisjointLoader(pred_dataset, batch_size=batch_size, epochs=1, shuffle=False)\n",
+    "loader_pred = DisjointLoader(\n",
+    "    pred_dataset, batch_size=batch_size, epochs=1, shuffle=False\n",
+    ")\n",
     "preds = model.predict(loader_pred.load(), use_multiprocessing=True)"
    ]
   },
@@ -931,17 +922,11 @@
     "\n",
     "kloppy_df = kloppy_dataset.to_df()\n",
     "\n",
-    "preds_df = pd.DataFrame({\n",
-    "    \"frame_id\": [x.id for x in pred_dataset],\n",
-    "    \"y\": preds.flatten()\n",
-    "})\n",
-    "\n",
-    "kloppy_df = pd.merge(\n",
-    "    kloppy_df,\n",
-    "    preds_df,\n",
-    "    on='frame_id',\n",
-    "    how='left'\n",
-    ")\n"
+    "preds_df = pd.DataFrame(\n",
+    "    {\"frame_id\": [x.id for x in pred_dataset], \"y\": preds.flatten()}\n",
+    ")\n",
+    "\n",
+    "kloppy_df = pd.merge(kloppy_df, preds_df, on=\"frame_id\", how=\"left\")"
    ]
   }
  ],