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 forward(self, x):
      fea_list = []
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.maxpool(x)
      x = self.layer1(x)
      fea_list.append(x)
      x = self.layer2(x)
      fea_list.append(x)
      x = self.layer3(x)
      fea_list.append(x)
      x = self.layer4(x)
      fea_list.append(x)
  
      return fea_list
  
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 3 for calling the ResNet obj's underlying member method code with example usage: obj.forward(x) and returns: fea_list