code: def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
      norm_layer = self._norm_layer
      downsample = None
      previous_dilation = self.dilation
      if dilate:
          self.dilation *= stride
          stride = 1
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2D(
                  self.inplanes,
                  planes * block.expansion,
                  1,
                  stride=stride,
                  bias_attr=False,
              ),
              norm_layer(planes * block.expansion),
          )
  
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              downsample,
              1,
              64,
              previous_dilation,
              norm_layer,
          )
      )
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(
              block(self.inplanes, planes, norm_layer=norm_layer)
          )
  
      return nn.Sequential(*layers)
  
imports: import os
  from typing import Union
  import numpy
  import paddle
  import paddle.nn
  import paddlehub.vision.transforms
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride, dilate) and returns: nn