CI: black 35, 36

teachopencadd/talktorials/T035_graph_neural_networks/talktorial.ipynb

@@ -369,26 +369,25 @@
    "outputs": [],
    "source": [
     "class SelectTarget(BaseTransform):\n",
-    "    \n",
     "    def __init__(self, target_index: int) -> None:\n",
     "        super().__init__()\n",
     "        self.target_index = target_index\n",
-    "    \n",
+    "\n",
     "    def forward(self, data):\n",
     "        data.y = data.y[:, self.target_index]\n",
     "        return data\n",
     "\n",
+    "\n",
     "class TargetNormalization(BaseTransform):\n",
-    "    \n",
     "    def __init__(self, mean: torch.Tensor, std: torch.Tensor) -> None:\n",
     "        super().__init__()\n",
     "        self.mean = mean\n",
     "        self.std = std\n",
-    "    \n",
+    "\n",
     "    def forward(self, data):\n",
     "        data.y = (data.y - self.mean) / self.std\n",
     "        return data\n",
-    "   \n",
+    "\n",
     "    @classmethod\n",
     "    def fit(cls, dataset, dim: int = 0, cat_dim: int = 0) -> \"TargetNormalization\":\n",
     "        y = torch.cat([dataset.get(i).y for i in range(len(dataset))], dim=cat_dim)\n",

teachopencadd/talktorials/T036_e3_equivariant_gnn/talktorial.ipynb

@@ -803,26 +803,25 @@
    "outputs": [],
    "source": [
     "class SelectTarget(BaseTransform):\n",
-    "    \n",
     "    def __init__(self, target_index: int) -> None:\n",
     "        super().__init__()\n",
     "        self.target_index = target_index\n",
-    "    \n",
+    "\n",
     "    def forward(self, data):\n",
     "        data.y = data.y[:, self.target_index]\n",
     "        return data\n",
     "\n",
+    "\n",
     "class TargetNormalization(BaseTransform):\n",
-    "    \n",
     "    def __init__(self, mean: torch.Tensor, std: torch.Tensor) -> None:\n",
     "        super().__init__()\n",
     "        self.mean = mean\n",
     "        self.std = std\n",
-    "    \n",
+    "\n",
     "    def forward(self, data):\n",
     "        data.y = (data.y - self.mean) / self.std\n",
     "        return data\n",
-    "   \n",
+    "\n",
     "    @classmethod\n",
     "    def fit(cls, dataset, dim: int = 0, cat_dim: int = 0) -> \"TargetNormalization\":\n",
     "        y = torch.cat([dataset.get(i).y for i in range(len(dataset))], dim=cat_dim)\n",
@@ -876,7 +875,7 @@
     "        self.test_split = self.shuffled_index[\n",
     "            int(self.num_examples * (train_ratio + val_ratio)) : self.num_examples\n",
     "        ]\n",
-    "    \n",
+    "\n",
     "    def _dataset(self):\n",
     "        return QM9(\n",
     "            DATA,\n",
@@ -893,12 +892,14 @@
     "            DATA,\n",
     "            pre_filter=lambda data: num_heavy_atoms(data) < 9,\n",
     "            pre_transform=add_complete_graph_edge_index,\n",
-    "            transform=Compose([normalize_target, select_target])\n",
+    "            transform=Compose([normalize_target, select_target]),\n",
     "        )\n",
-    "   \n",
-    "    @cached_property \n",
+    "\n",
+    "    @cached_property\n",
     "    def target(self):\n",
-    "        return torch.cat([self.dataset.get(i).y[:, self.target_idx] for i in range(len(self.dataset))], dim=0)\n",
+    "        return torch.cat(\n",
+    "            [self.dataset.get(i).y[:, self.target_idx] for i in range(len(self.dataset))], dim=0\n",
+    "        )\n",
     "\n",
     "    def loader(self, split, **loader_kwargs) -> DataLoader:\n",
     "        dataset = self.dataset[split]\n",