code: def __init__(
      self, block_channels, side_channels, groups, num_classes=1000
  ):
      super(MENet, self).__init__()
      input_channels = 3
      block_layers = [4, 8, 4]
  
      self.features = nn.Sequential()
      self.features.add_module(
          "init_block",
          ShuffleInitBlock(
              in_channels=input_channels, out_channels=block_channels[0]
          ),
      )
  
      for i in range(len(block_channels) - 1):
          stage = nn.Sequential()
          in_channels_i = block_channels[i]
          out_channels_i = block_channels[i + 1]
          for j in range(block_layers[i]):
              stage.add_module(
                  "unit_{}".format(j + 1),
                  MEModule(
                      in_channels=(
                          in_channels_i if j == 0 else out_channels_i
                      ),
                      out_channels=out_channels_i,
                      side_channels=side_channels,
                      groups=groups,
                      downsample=(j == 0),
                      ignore_group=(i == 0 and j == 0),
                  ),
              )
          self.features.add_module("stage_{}".format(i + 1), stage)
  
      self.features.add_module(
          "final_pool", nn.AvgPool2d(kernel_size=7)
      )
  
      self.output = nn.Linear(
          in_features=block_channels[-1], out_features=num_classes
      )
  
      self._init_params()
  
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 MENet object from inherited base classes: nn with example: obj = MENet(block_channels, side_channels, groups, num_classes)