class_docstr: ResNet model from
  `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
  
  Args:
      Block (BasicBlock|BottleneckBlock): Block module of model.
      depth (int, optional): Layers of resnet, Default: 50.
      width (int, optional): Base width per convolution group for each convolution block, Default: 64.
      in_channels (int, optional): The channels of input image. Default: 3.
      groups (int, optional): Number of groups for each convolution block, Default: 1.
      ssl_pretrained (bool, optinal) : Use self-supervised pretrained model or not. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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,
              self.groups,
              self.base_width,
              previous_dilation,
              norm_layer,
          )
      )
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  groups=self.groups,
                  base_width=self.base_width,
                  norm_layer=norm_layer,
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import os
  from urllib.parse import urlparse
  import paddle
  import paddle.nn
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils, logger
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