code: def __init__(
      self, in_feature, out_feature, stride, padding_mode="zeros"
  ):
      super(Pooling, self).__init__()
  
      self.conv = nn.Conv2d(
          in_feature,
          out_feature,
          kernel_size=stride + 1,
          padding=stride // 2,
          stride=stride,
          padding_mode=padding_mode,
          groups=in_feature,
      )
      self.fc = nn.Linear(in_feature, out_feature)
  
imports: import math
  import re
  from functools import partial
  from typing import Sequence, Tuple
  import torch
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_, to_2tuple, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 1 to create a new Pooling object from inherited base classes: nn with example: obj = Pooling(in_feature, out_feature, stride, padding_mode)