code: def _make_MG_unit(self, block, planes, blocks=None, stride=1, rate=1):
      if blocks is None:
          blocks = [1, 2, 4]
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2d(
                  self.inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(planes * block.expansion),
          )
  
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              rate=blocks[0] * rate,
              downsample=downsample,
          )
      )
      self.inplanes = planes * block.expansion
      for i in range(1, len(blocks)):
          layers.append(
              block(
                  self.inplanes, planes, stride=1, rate=blocks[i] * rate
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
label_desc: Use image node_id 3 for calling the ResNet obj's underlying member method code with example usage: obj._make_MG_unit(block, planes, blocks, stride, rate) and returns: nn