code: def _make_layer(
      self,
      block,
      planes,
      blocks,
      groups,
      reduction,
      stride=1,
      downsample_kernel_size=1,
      downsample_padding=0,
  ):
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2d(
                  self.inplanes,
                  planes * block.expansion,
                  kernel_size=downsample_kernel_size,
                  stride=stride,
                  padding=downsample_padding,
                  bias=False,
              ),
              nn.BatchNorm2d(planes * block.expansion),
          )
  
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              groups,
              reduction,
              stride,
              downsample,
          )
      )
      self.inplanes = planes * block.expansion
      for i in range(1, blocks):
          layers.append(block(self.inplanes, planes, groups, reduction))
  
      return nn.Sequential(*layers)
  
imports: from __future__ import print_function, division, absolute_import
  import math
  from collections import OrderedDict
  import torch.nn
  from torch.utils import model_zoo
label_desc: Use image node_id 2 for calling the SENet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, groups, reduction, stride, downsample_kernel_size, downsample_padding) and returns: nn