Consider the forward pass
def forward(self, x):
  x = self.pool(F.relu(self.conv1(x)))
  x = self.pool(F.relu(self.conv2(x)))
  x = x.view(-1, 16 * 5 * 5)
  x = F.relu(self.fc1(x))
  x = F.relu(self.fc2(x))
  x = self.fc3(x)
  return x

In model forward passes, if we use tensor transformations such as view(as shown in the example), transpose, permute,reshape - does this effect grad values?
If no, how the mapping between the original and transformed tensor are maintained?