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# torch.amax

torch.amax(input, dim, keepdim=False, *, out=None) → Tensor

  Returns the maximum value of each slice of the `input` tensor in the given dimension(s) `dim`.

  Note

  The difference between `max`/`min` and `amax`/`amin` is:

    - `amax`/`amin` supports reducing on multiple dimensions,

    - `amax`/`amin` does not return indices,

    - `amax`/`amin` evenly distributes gradient between equal values, while `max(dim)`/`min(dim)` propagates gradient only to a single index in the source tensor.

  If `keepdim` is `True`, the output tensor is of the same size as `input` except in the dimension(s) `dim` where it is of size 1. Otherwise, `dim` is squeezed (see `torch.squeeze()`), resulting in the output tensor having 1 (or `len(dim)`) fewer dimension(s).

  Parameters:

    - input (Tensor) – the input tensor.

    - dim (int or tuple of ints) – the dimension or dimensions to reduce.

    - keepdim (bool) – whether the output tensor has `dim` retained or not.

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 0.8177,  1.4878, -0.2491,  0.9130],
              [-0.7158,  1.1775,  2.0992,  0.4817],
              [-0.0053,  0.0164, -1.3738, -0.0507],
              [ 1.9700,  1.1106, -1.0318, -1.0816]])
      >>> torch.amax(a, 1)
      tensor([1.4878, 2.0992, 0.0164, 1.9700])

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