code: def __init__(self, in_channels, out_channels, norm_layer):
      super(_AsppPooling, self).__init__()
  
      self.gap = nn.Sequential(
          nn.AdaptiveAvgPool2d((1, 1)),
          nn.Conv2d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=1,
              bias=False,
          ),
          norm_layer(out_channels),
          nn.ReLU(),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from contextlib import ExitStack
  from .resnet import ResNetBackbone
  from .basic_blocks import select_activation_function
  from .basic_blocks import SeparableConv2d
label_desc: Use image node_id 1 to create a new _AsppPooling object from inherited base classes: nn with example: obj = _AsppPooling(in_channels, out_channels, norm_layer)