code: def adaptive_avg_pool3d(
      input: Tensor, output_size: BroadcastingList2[int]
  ) -> Tensor:
      r"""
      Applies a 3D adaptive average pooling over a quantized input signal composed
      of several quantized input planes.
  
      .. note:: The input quantization parameters propagate to the output.
  
      See :class:`~torch.ao.nn.quantized.AdaptiveAvgPool3d` for details and output shape.
  
      Args:
          output_size: the target output size (single integer or
                       double-integer tuple)
      """
      if not input.is_quantized:
          raise ValueError(
              "Input to 'quantized.functional.adaptive_avg_pool3d' must be quantized!"
          )
      return torch.nn.functional.adaptive_avg_pool3d(input, output_size)
  
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 4 for calling a global function with example usage: adaptive_avg_pool3d(input, output_size) and returns: torch