code: def _make_layer(
      self, block, planes, blocks, stride=1, dilate=False, dropblock=0.0
  ):
      downsample = None
      previous_dilation = self.dilation
      if dilate:
          self.dilation *= stride
          stride = 1
      if stride != 1 or self.inplanes != planes * block.expansion:
          if self.stem_type != "D":
              downsample = nn.Sequential(
                  conv1x1(
                      self.inplanes, planes * block.expansion, stride
                  ),
                  nn.BatchNorm2d(
                      planes * block.expansion, eps=self.eps
                  ),
              )
          else:
              # avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
              avg_stride = stride
              pool = nn.AvgPool2d(
                  2, avg_stride, ceil_mode=True, count_include_pad=False
              )
              downsample = nn.Sequential(
                  *[
                      pool,
                      conv1x1(
                          self.inplanes,
                          planes * block.expansion,
                          stride=1,
                      ),
                      nn.BatchNorm2d(
                          planes * block.expansion, eps=self.eps
                      ),
                  ]
              )
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              downsample,
              self.groups,
              self.base_width,
              previous_dilation,
              self.eps,
              dropblock,
          )
      )
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  groups=self.groups,
                  base_width=self.base_width,
                  dilation=self.dilation,
                  eps=self.eps,
                  dropblock=dropblock,
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  import logging
label_desc: Use image node_id 2 for calling the IResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride, dilate, dropblock) and returns: nn