The copy changes elements in place, so that is useful when you want to do that:
big_tensor = torch.rand(batch, n_chan+1, dim)

# I want to fill the last channel with the mean of the others (as an example):
last_channel = big_tensor.select(1, -1) # Get the last channel in-place.
last_channel.copy_(big_tensor.narrow(1, 0, n_chan).mean(1)) # Change the last channel

# Now you can use big_tensor, where the last channel has been changed.
out = my_net(big_tensor)