Hello,

I have been struggling with determining how the batching of the Dataloader works with nn.Module. As far as I understand any last 2 dimensions of the given tensor are accepted as an input however I cannot see this to be the case on the following piece of code:

```
class FNet(nn.Module):
def __init__(self, **kwargs):
super(FNet, self).__init__()
self.fc1 = nn.Linear(32678, 1024)
self.fc2 = nn.Linear(1024, 256)
self.fc3 = nn.Linear(256, 1)
def forward(self, X):
X = F.relu( self.fc1( F.relu(X) ) )
X = F.relu( self.fc2(X) )
return F.sigmoid( self.fc1(X) )
```

```
train_dataloader = DataLoader(train_dataset, batch_size=train_batch, shuffle=True)
model.train()
for batch_idx, (data, labels) in enumerate(loader):
optimizer.zero_grad()
print(data.shape, data.dtype)
data = data.to(device)
labels = labels.to(device)
# Forward pass: Compute predicted y by passing x to the model
data = data.reshape(train_batch, 1, 32768).float()
labels_pred = model(data)
loss = criterion(labels_pred, labels.long())
loss.backward()
optimizer.step()
```

On the code given above, I try to resize the input to get a size of (batch_size, 1, 32768) so that 1x32768 should be gotten as input dimensions. However I get the following error:

RuntimeError: size mismatch, m1: [4 x 32768], m2: [32678 x 1024]

I will be grateful for any response. Thank you.