code: def get_menet(
      first_block_channels,
      side_channels,
      groups,
      pretrained=False,
      **kwargs,
  ):
      if first_block_channels == 108:
          block_channels = [12, 108, 216, 432]
      elif first_block_channels == 128:
          block_channels = [12, 128, 256, 512]
      elif first_block_channels == 160:
          block_channels = [16, 160, 320, 640]
      elif first_block_channels == 228:
          block_channels = [24, 228, 456, 912]
      elif first_block_channels == 256:
          block_channels = [24, 256, 512, 1024]
      elif first_block_channels == 348:
          block_channels = [24, 348, 696, 1392]
      elif first_block_channels == 352:
          block_channels = [24, 352, 704, 1408]
      elif first_block_channels == 456:
          block_channels = [48, 456, 912, 1824]
      else:
          raise ValueError(
              "The {} of `first_block_channels` is not supported".format(
                  first_block_channels
              )
          )
  
      if pretrained:
          raise ValueError("Pretrained model is not supported")
  
      net = MENet(
          block_channels=block_channels,
          side_channels=side_channels,
          groups=groups,
          **kwargs,
      )
      return net
  
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 6 for calling a global function with example usage: get_menet(first_block_channels, side_channels, groups, pretrained) and returns: net