code: def __init__(
      self,
      in_channels,
      out_channels,
      side_channels,
      groups,
      downsample,
      ignore_group,
  ):
      super(MEModule, self).__init__()
      self.downsample = downsample
      mid_channels = out_channels // 4
  
      if downsample:
          out_channels -= in_channels
  
      # residual branch
      self.compress_conv1 = group_conv1x1(
          in_channels=in_channels,
          out_channels=mid_channels,
          groups=(1 if ignore_group else groups),
      )
      self.compress_bn1 = nn.BatchNorm2d(num_features=mid_channels)
      self.c_shuffle = ChannelShuffle(
          channels=mid_channels, groups=(1 if ignore_group else groups)
      )
      self.dw_conv2 = depthwise_conv3x3(
          channels=mid_channels, stride=(2 if self.downsample else 1)
      )
      self.dw_bn2 = nn.BatchNorm2d(num_features=mid_channels)
      self.expand_conv3 = group_conv1x1(
          in_channels=mid_channels,
          out_channels=out_channels,
          groups=groups,
      )
      self.expand_bn3 = nn.BatchNorm2d(num_features=out_channels)
      if downsample:
          self.avgpool = nn.AvgPool2d(
              kernel_size=3, stride=2, padding=1
          )
      self.activ = nn.ReLU(inplace=True)
  
      # fusion branch
      self.s_merge_conv = conv1x1(
          in_channels=mid_channels, out_channels=side_channels
      )
      self.s_merge_bn = nn.BatchNorm2d(num_features=side_channels)
      self.s_conv = conv3x3(
          in_channels=side_channels,
          out_channels=side_channels,
          stride=(2 if self.downsample else 1),
      )
      self.s_conv_bn = nn.BatchNorm2d(num_features=side_channels)
      self.s_evolve_conv = conv1x1(
          in_channels=side_channels, out_channels=mid_channels
      )
      self.s_evolve_bn = nn.BatchNorm2d(num_features=mid_channels)
  
imports: import numpy
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
label_desc: Use image node_id 1 to create a new MEModule object from inherited base classes: nn with example: obj = MEModule(in_channels, out_channels, side_channels, groups, downsample, ignore_group)