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# torch.narrow_copy

torch.narrow_copy(input, dim, start, length, *, out=None) → Tensor

  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.

  Parameters:

    - input (Tensor) – the tensor to narrow

    - dim (int) – the dimension along which to narrow

    - start (int) – index of the element to start the narrowed dimension from. Can be negative, which means indexing from the end of dim

    - length (int) – length of the narrowed dimension, must be weakly positive

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> 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)

  See also

  `torch.narrow()` for a non copy variant

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