code: def cfm_branch(in_channels: int, out_channels: int, kernel_size: int):
      return nn.Sequential(
          nn.Conv2d(
              in_channels,
              out_channels,
              kernel_size=kernel_size,
              padding=kernel_size // 2,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(inplace=True),
          nn.Conv2d(
              out_channels,
              out_channels,
              kernel_size=kernel_size,
              padding=kernel_size // 2,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
      )
  
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 for calling a global function with example usage: cfm_branch(in_channels, out_channels, kernel_size) and returns: nn