Same as Tensor.narrow() except this returns a copy rather than shared storage. This is primarily for sparse tensors, which do not have a shared-storage narrow method.
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> torch.narrow_copy(x, 0, 0, 2)
tensor([[ 1, 2, 3],
[ 4, 5, 6]])
>>> torch.narrow_copy(x, 1, 1, 2)
tensor([[ 2, 3],
[ 5, 6],
[ 8, 9]])
>>> s = torch.arange(16).reshape(2, 2, 2, 2).to_sparse(2)
>>> torch.narrow_copy(s, 0, 0, 1)
tensor(indices=tensor([[0, 0],
[0, 1]]),
values=tensor([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]]),
size=(1, 2, 2, 2), nnz=2, layout=torch.sparse_coo)
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