code: def avg_pool2d_same(
      x,
      kernel_size: List[int],
      stride: List[int],
      padding: List[int] = (0, 0),
      ceil_mode: bool = False,
      count_include_pad: bool = True,
  ):
      # FIXME how to deal with count_include_pad vs not for external padding?
      x = pad_same(x, kernel_size, stride)
      return F.avg_pool2d(
          x, kernel_size, stride, (0, 0), ceil_mode, count_include_pad
      )
  
file_path: pytorch-image-models/timm/layers/pool2d_same.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import List, Tuple, Optional
  from .helpers import to_2tuple
  from .padding import pad_same, get_padding_value
label_desc: Use image node_id 1 for calling a global function with example usage: avg_pool2d_same(x, kernel_size, stride, padding, ceil_mode, count_include_pad) and returns: F