# tensorplay.functional.narrow_copy

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.narrow_copy.html

```python
tensorplay.functional.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](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the tensor to narrow

- dim ([int](https://docs.python.org/3/builtins/functions.html#int)) – the dimension along which to narrow

- start ([int](https://docs.python.org/3/builtins/functions.html#int)) – index of the element to start the narrowed dimension from. Can be negative, which means indexing from the end of dim

- length ([int](https://docs.python.org/3/builtins/functions.html#int)) – length of the narrowed dimension, must be weakly positive

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> x = tensorplay.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> tensorplay.narrow_copy(x, 0, 0, 2)
tensor([[ 1,  2,  3],
        [ 4,  5,  6]])
>>> tensorplay.narrow_copy(x, 1, 1, 2)
tensor([[ 2,  3],
        [ 5,  6],
        [ 8,  9]])
>>> s = tensorplay.arange(16).reshape(2, 2, 2, 2).to_sparse(2)
>>> tensorplay.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=tensorplay.sparse_coo)
```

> **See also**
>
> tensorplay.narrow() for a non copy variant