code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_sizes=[3, 5, 7, 11],
  ):
      super().__init__()
      self.gp_branch = nn.Sequential(
          nn.AdaptiveAvgPool2d(1),
          nn.Conv2d(
              in_channels, out_channels, kernel_size=1, bias=False
          ),
          nn.BatchNorm2d(out_channels),
      )
  
      self.conv_branches = nn.ModuleList(
          ds_cfm_branch(in_channels, out_channels, ks)
          for ks in kernel_sizes
      )
  
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..dsconv import DepthwiseSeparableConv2d
  from ..interfaces import AbstractDecoder, FeatureMapsSpecification
label_desc: Use image node_id 1 to create a new CFM object from inherited base classes: nn with example: obj = CFM(in_channels, out_channels, kernel_sizes)