I think freezing specific parts of a parameter is not possible in PyTorch because requires_grad flag is set on each Parameter(collection of weights), not each weight.
One possible approach is manually zeroing gradient before you call optimizer function.
After you calculate gradient using backward() function, call
param.grad[:, 1:10, :, :] = 0

to achieve you want.
Further, you can automatize this method using backward_hook in PyTorch.
You can check this link if you want.
http://pytorch.org/tutorials/beginner/former_torchies/nn_tutorial.html 433