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# torch.Tensor.unfold

Tensor.unfold(dimension, size, step) → Tensor

  Returns a view of the original tensor which contains all slices of size `size` from `self` tensor in the dimension `dimension`.

  Step between two slices is given by `step`.

  If sizedim is the size of dimension `dimension` for `self`, the size of dimension `dimension` in the returned tensor will be (sizedim - size) / step + 1.

  An additional dimension of size `size` is appended in the returned tensor.

  Parameters:

    - dimension (int) – dimension in which unfolding happens

    - size (int) – the size of each slice that is unfolded

    - step (int) – the step between each slice

  Example:

      >>> x = torch.arange(1., 8)
      >>> x
      tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.])
      >>> x.unfold(0, 2, 1)
      tensor([[ 1.,  2.],
              [ 2.,  3.],
              [ 3.,  4.],
              [ 4.,  5.],
              [ 5.,  6.],
              [ 6.,  7.]])
      >>> x.unfold(0, 2, 2)
      tensor([[ 1.,  2.],
              [ 3.,  4.],
              [ 5.,  6.]])

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