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# torch.aminmax

torch.aminmax(input, *, dim=None, keepdim=False, out=None) -> (Tensor min, Tensor max)

  Computes the minimum and maximum values of the `input` tensor.

  Parameters:

    input (Tensor) – The input tensor

  Keyword Arguments:

    - dim (Optional[int]) – The dimension along which to compute the values. If None, computes the values over the entire `input` tensor. Default is None.

    - keepdim (bool) – If True, the reduced dimensions will be kept in the output tensor as dimensions with size 1 for broadcasting, otherwise they will be removed, as if calling (`torch.squeeze()`). Default is False.

    - out (Optional[Tuple[Tensor, Tensor]]) – Optional tensors on which to write the result. Must have the same shape and dtype as the expected output. Default is None.

  Returns:

    A named tuple (min, max) containing the minimum and maximum values.

  Raises:

    RuntimeError – If any of the dimensions to compute the values over has size 0.

  Note

  NaN values are propagated to the output if at least one value is NaN.

  See also

  `torch.amin()` computes just the minimum value `torch.amax()` computes just the maximum value

  Example:

      >>> torch.aminmax(torch.tensor([1, -3, 5]))
      torch.return_types.aminmax(
      min=tensor(-3),
      max=tensor(5))

      >>> # aminmax propagates NaNs
      >>> torch.aminmax(torch.tensor([1, -3, 5, torch.nan]))
      torch.return_types.aminmax(
      min=tensor(nan),
      max=tensor(nan))

      >>> t = torch.arange(10).view(2, 5)
      >>> t
      tensor([[0, 1, 2, 3, 4],
              [5, 6, 7, 8, 9]])
      >>> t.aminmax(dim=0, keepdim=True)
      torch.return_types.aminmax(
      min=tensor([[0, 1, 2, 3, 4]]),
      max=tensor([[5, 6, 7, 8, 9]]))

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