code: def _avg_pool(name, expand_size):
      @symbolic_helper.quantized_args(
          True, False, False, False, False, False, False
      )
      @symbolic_helper.parse_args(
          "v", "is", "is", "is", "i", "i", "none"
      )
      @_beartype.beartype
      def symbolic_fn(
          g,
          input: _C.Value,
          kernel_size: Sequence[int],
          stride: Sequence[int],
          padding: Union[int, Sequence[int]],
          ceil_mode: int,
          count_include_pad: int,
          divisor_override=None,
      ):
          kernel_shape, strides, pads = _adjust_attributes_of_avg_pool(
              expand_size, kernel_size, stride, padding
          )
  
          result = g.op(
              "AveragePool",
              input,
              ceil_mode_i=ceil_mode,
              count_include_pad_i=count_include_pad,
              kernel_shape_i=kernel_shape,
              pads_i=pads,
              strides_i=strides,
          )
  
          return result
  
      return symbolic_fn
  
imports: from __future__ import annotations
  import functools
  import sys
  import warnings
  from typing import List, Optional, Sequence, Tuple, Union
  import torch
  import torch._C._onnx
  import torch.onnx
  from torch import _C
  from torch.onnx import _constants, _type_utils, errors, symbolic_helper, symbolic_opset9
  from torch.onnx._globals import GLOBALS
  from torch.onnx._internal import _beartype, jit_utils, registration
label_desc: Use image node_id 12 for calling a global function with example usage: _avg_pool(name, expand_size) and returns: symbolic_fn, result