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# avg_pool2d

class torch.ao.nn.quantized.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)

  Applies 2D average-pooling operation in k H×k W kH \times kW k H×kW regions by step size s H×s W sH \times sW sH×s W steps. The number of output features is equal to the number of input planes.

  Note

  The input quantization parameters propagate to the output.

  See `AvgPool2d` for details and output shape.

  Parameters:

    - input – quantized input tensor (minibatch,in_channels,i H,i W)(\text{minibatch} , \text{in\_channels} , iH , iW)(minibatch,in_channels,i H,iW)

    - kernel_size – size of the pooling region. Can be a single number or a tuple (kH, kW)

    - stride – stride of the pooling operation. Can be a single number or a tuple (sH, sW). Default: `kernel_size`

    - padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0

    - ceil_mode – when True, will use ceil instead of floor in the formula to compute the output shape. Default: `False`

    - count_include_pad – when True, will include the zero-padding in the averaging calculation. Default: `True`

    - divisor_override – if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None

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