class_docstr: Implements AdaptiveAvgPool3d with any output (T, H, W) size. This operator is
  supported by QNNPACK for mobile CPU with resonable efficiency, and no change is
  made upon convert(). If the output (T, H, W) = (1, 1, 1), use AdaptiveAvgPool3dOutSize1
  for better efficiency.
  Args:
      output_size (int or tuple): when it is a tuple, the output (T, H, W) of pool
          will be equal to output_size. When it is an int, the output (T, H, W)
          will be equal to (output_size, output_size, output_size).
class_docstr_tok: ["Implements","AdaptiveAvgPool3d","with","any","output","(","T",",","H",",","W",")","size",".","This","operator","is","supported","by","QNNPACK","for","mobile","CPU","with","resonable","efficiency",",","and","no","change","is","made","upon","convert","(",")",".","If","the","output","(","T",",","H",",","W",")","=","(","1",",","1",",","1",")",",","use","AdaptiveAvgPool3dOutSize1","for","better","efficiency",".","Args",":","output_size","(","int","or","tuple",")",":","when","it","is","a","tuple",",","the","output","(","T",",","H",",","W",")","of","pool","will","be","equal","to","output_size",".","When","it","is","an","int",",","the","output","(","T",",","H",",","W",")","will","be","equal","to","(","output_size",",","output_size",",","output_size",")","."]
code: def __init__(
      self,
      output_size: Union[int, Tuple],
  ):
      super().__init__(model=nn.AdaptiveAvgPool3d(output_size))
  
imports: from typing import Tuple, Union
  import torch.nn
  from pytorchvideo.accelerator.efficient_blocks.efficient_block_base import EfficientBlockBase
  from pytorchvideo.accelerator.efficient_blocks.no_op_convert_block import NoOpConvertBlock
label_desc: Use image node_id 1 to create a new AdaptiveAvgPool3d object from inherited base classes: NoOpConvertBlock with example: obj = AdaptiveAvgPool3d(output_size)