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# torch.quantized_max_pool1d

torch.quantized_max_pool1d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) → Tensor

  Applies a 1D max pooling over an input quantized tensor composed of several input planes.

  Parameters:

    - input (Tensor) – quantized tensor

    - kernel_size (list of python:int) – the size of the sliding window

    - stride (`list of int`, optional) – the stride of the sliding window

    - padding (`list of int`, optional) – padding to be added on both sides, must be >= 0 and <= kernel_size / 2

    - dilation (`list of int`, optional) – The stride between elements within a sliding window, must be > 0. Default 1

    - ceil_mode (bool, optional) – If True, will use ceil instead of floor to compute the output shape. Defaults to False.

  Returns:

    A quantized tensor with max_pool1d applied.

  Return type:

    Tensor

  Example:

      >>> qx = torch.quantize_per_tensor(torch.rand(2, 2), 1.5, 3, torch.quint8)
      >>> torch.quantized_max_pool1d(qx, [2])
      tensor([[0.0000],
              [1.5000]], size=(2, 1), dtype=torch.quint8,
          quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3)

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