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# torch.diff

torch.diff(input, n=1, dim=- 1, prepend=None, append=None) → Tensor

  Computes the n-th forward difference along the given dimension.

  The first-order differences are given by out[i] = input[i + 1] - input[i]. Higher-order differences are calculated by using `torch.diff()` recursively.

  Parameters:

    - input (Tensor) – the tensor to compute the differences on

    - n (int, optional) – the number of times to recursively compute the difference

    - dim (int, optional) – the dimension to compute the difference along. Default is the last dimension.

    - prepend (Tensor, optional) – values to prepend or append to `input` along `dim` before computing the difference. Their dimensions must be equivalent to that of input, and their shapes must match input’s shape except on `dim`.

    - append (Tensor, optional) – values to prepend or append to `input` along `dim` before computing the difference. Their dimensions must be equivalent to that of input, and their shapes must match input’s shape except on `dim`.

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> a = torch.tensor([1, 3, 2])
      >>> torch.diff(a)
      tensor([ 2, -1])
      >>> b = torch.tensor([4, 5])
      >>> torch.diff(a, append=b)
      tensor([ 2, -1,  2,  1])
      >>> c = torch.tensor([[1, 2, 3], [3, 4, 5]])
      >>> torch.diff(c, dim=0)
      tensor([[2, 2, 2]])
      >>> torch.diff(c, dim=1)
      tensor([[1, 1],
              [1, 1]])

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