The problem on this line is not the torch.cat but the model(data.view(-1, 1, 64, 64).float()) as you can see that the rest of the stack trace points inside the model forward and to the batchnorm.
So you want to double check that input and output of the batchnorm and make sure they are not updated inplace (by a ReLU(inplace=True) for example).