Ran formatter

train.py

@@ -11,10 +11,9 @@
 class Net(nn.Module):
     def __init__(self):
 
-
         super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(1,   32, 3, 1)
-        self.conv2 = nn.Conv2d( 32, 64, 3, 1)
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
         self.dropout1 = nn.Dropout(0.25)
         self.dropout2 = nn.Dropout(0.5)
         self.fc1 = nn.Linear(9216, 128)
@@ -36,14 +35,14 @@ def forward(self, x):
         return output
 
 
-def train(args,    model, device, train_loader, optimizer, epoch):
+def train(args, model, device, train_loader, optimizer, epoch):
     model.train()
     for batch_idx, (data, target) in enumerate(train_loader):
 
-        data, target = data.to(device),   target.to(device)
+        data, target = data.to(device), target.to(device)
         optimizer.zero_grad()
         output = model(data)
-        loss = F.nll_loss(   output, target)
+        loss = F.nll_loss(output, target)
         loss.backward()
         optimizer.step()
         if batch_idx % args.log_interval == 0:
@@ -52,7 +51,7 @@ def train(args,    model, device, train_loader, optimizer, epoch):
                     epoch,
                     batch_idx * len(data),
                     len(train_loader.dataset),
-                    100.0 *    batch_idx / len(train_loader),
+                    100.0 * batch_idx / len(train_loader),
                     loss.item(),
                 )
             )
@@ -70,11 +69,11 @@ def test(model, device, test_loader, epoch):
 
             data, target = data.to(device), target.to(device)
             output = model(data)
-            test_loss += F.nll_loss(   output,   target,   reduction="sum").item()  # sum up batch loss
+            test_loss += F.nll_loss(output, target, reduction="sum").item()  # sum up batch loss
             pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
-            correct += pred.eq(target.view_as(pred)  ).sum().item()
+            correct += pred.eq(target.view_as(pred)).sum().item()
 
-    test_loss /= len(test_loader.dataset  )
+    test_loss /= len(test_loader.dataset)
 
     print(
         "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
@@ -132,7 +131,7 @@ def main():
     model = Net().to(device)
     optimizer = optim.Adam(model.parameters(), lr=args.lr)
 
-    scheduler = StepLR(optimizer,    step_size=1, gamma=args.gamma)
+    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
     for epoch in range(args.epochs):
         train(args, model, device, train_loader, optimizer, epoch)
         test(model, device, test_loader, epoch)