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# torch.as_strided

torch.as_strided(input, size, stride, storage_offset=None) → Tensor

  Create a view of an existing torch.Tensor `input` with specified `size`, `stride` and `storage_offset`.

  Warning

  Prefer using other view functions, like `torch.Tensor.expand()`, to setting a view’s strides manually with as_strided, as this function’s behavior depends on the implementation of a tensor’s storage. The constructed view of the storage must only refer to elements within the storage or a runtime error will be thrown, and if the view is “overlapped” (with multiple indices referring to the same element in memory) its behavior is undefined.

  Parameters:

    - input (Tensor) – the input tensor.

    - size (tuple or ints) – the shape of the output tensor

    - stride (tuple or ints) – the stride of the output tensor

    - storage_offset (int, optional) – the offset in the underlying storage of the output tensor. If `None`, the storage_offset of the output tensor will match the input tensor.

  Example:

      >>> x = torch.randn(3, 3)
      >>> x
      tensor([[ 0.9039,  0.6291,  1.0795],
              [ 0.1586,  2.1939, -0.4900],
              [-0.1909, -0.7503,  1.9355]])
      >>> t = torch.as_strided(x, (2, 2), (1, 2))
      >>> t
      tensor([[0.9039, 1.0795],
              [0.6291, 0.1586]])
      >>> t = torch.as_strided(x, (2, 2), (1, 2), 1)
      tensor([[0.6291, 0.1586],
              [1.0795, 2.1939]])

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