input signal composed of several input
    planes.

    In the simplest case, the output value of the layer with input size :math:`(N, C, D, H, W)`,
    output :math:`(N, C, D_{out}, H_{out}, W_{out})` and :attr:`kernel_size` :math:`(kD, kH, kW)`
    can be precisely described as:

    .. math::
        \begin{aligned}
            \text{out}(N_i, C_j, d, h, w) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
                                              & \frac{\text{input}(N_i, C_j, \text{stride}[0] \times d + k,
                                                      \text{stride}[1] \times h + m, \text{stride}[2] \times w + n)}
                                                     {kD \times kH \times kW}
        \end{aligned}

    If :attr:`padding` is non-zero, then the input is implicitly zero-padded on all three sides
    for :attr:`padding` number of points.

    Note:
        When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding
        or the input. Sliding windows that would start in the right padded region are ignored.

    The parameters :attr:`kernel_size`, :attr:`stride` can either be:

        - a single ``int`` -- in which case the same value is used for the depth, height and width dimension
        - a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
          the second `int` for the height dimension and the third `int` for the width dimension

    Args:
        kernel_size: the size of the window
        stride: the stride of the window. Default value is :attr:`kernel_size`
        padding: implicit zero padding to be added on all three sides
        ceil_mode: when True, will use `ceil` instead of `floor` to compute the output shape
        count_include_pad: when True, will include the zero-padding in the averaging calculation
        divisor_override: if specified, it will be used as divisor, otherwise :attr:`kernel_size` will be used

    Shape:
        - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`.
        - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or
          :math:`(C, D_{out}, H_{out}, W_{out})`, where

          .. math::
              D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] -
                    \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor

          .. math::
              H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] -
                    \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor

          .. math::
              W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] -
                    \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor

    Examples::

        >>> # pool of square window of size=3, stride=2
        >>> m = nn.AvgPool3d(3, stride=2)
        >>> # pool of non-square window
        >>> m = nn.AvgPool3d((3, 2, 2), stride=(2, 1, 2))
        >>> input = torch.randn(20, 16, 50, 44, 31)
        >>> output = m(input)
    re