code: def avg_pool3d(
      input,
      kernel_size,
      stride=None,
      padding=0,
      ceil_mode=False,
      count_include_pad=True,
      divisor_override=None,
  ):
      r"""
      Applies 3D average-pooling operation in :math:`kD \ times kH \times kW` regions by step size
      :math:`sD \times sH \times sW` steps. The number of output features is equal to the number of
      input planes.
  
      .. note:: The input quantization parameters propagate to the output.
  
      Args:
          input: quantized input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
          kernel_size: size of the pooling region. Can be a single number or a
            tuple `(kD, kH, kW)`
          stride: stride of the pooling operation. Can be a single number or a
            tuple `(sD, sH, sW)`. Default: :attr:`kernel_size`
          padding: implicit zero paddings on both sides of the input. Can be a
            single number or a tuple `(padD, 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
      """
      if not input.is_quantized:
          raise ValueError(
              "Input to 'quantized.avg_pool3d' must be quantized!"
          )
      return torch.nn.functional.avg_pool3d(
          input,
          kernel_size,
          stride,
          padding,
          ceil_mode,
          count_include_pad,
          divisor_override,
      )
  
imports: from typing import List, Optional
  import warnings
  import torch
  from torch import Tensor
  from torch.nn.modules.utils import _pair, _triple
  from torch.jit.annotations import BroadcastingList2
  from .modules.utils import _pair_from_first
label_desc: Use image node_id 2 for calling a global function with example usage: avg_pool3d(input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) and returns: torch