style: black formatting for precommit

demo-notebooks/guided-demos/download_mnist_datasets.py

@@ -16,27 +16,32 @@
 from torchvision.datasets import MNIST
 from torchvision import transforms
 
+
 def download_mnist_dataset(destination_dir):
     # Ensure the destination directory exists
     if not os.path.exists(destination_dir):
         os.makedirs(destination_dir)
 
     # Define transformations
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.1307,), (0.3081,))
-    ])
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
 
     # Download the training data
-    train_set = MNIST(root=destination_dir, train=True, download=True, transform=transform)
+    train_set = MNIST(
+        root=destination_dir, train=True, download=True, transform=transform
+    )
 
     # Download the test data
-    test_set = MNIST(root=destination_dir, train=False, download=True, transform=transform)
+    test_set = MNIST(
+        root=destination_dir, train=False, download=True, transform=transform
+    )
 
     print(f"MNIST dataset downloaded in {destination_dir}")
 
+
 # Specify the directory where you
 script_dir = os.path.dirname(os.path.abspath(__file__))
 destination_dir = script_dir + "/mnist_datasets"
 
-download_mnist_dataset(destination_dir)
+download_mnist_dataset(destination_dir)

demo-notebooks/guided-demos/mnist_disconnected.py

@@ -121,7 +121,9 @@ def prepare_data(self):
     def setup(self, stage=None):
         # Assign train/val datasets for use in dataloaders
         if stage == "fit" or stage is None:
-            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform, download=False)
+            mnist_full = MNIST(
+                self.data_dir, train=True, transform=self.transform, download=False
+            )
             self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
 
         # Assign test dataset for use in dataloader(s)