class_docstr: The ResNet_vd implementation based on PaddlePaddle.
  
  The original article refers to Jingdong
  Tong He, et, al. "Bag of Tricks for Image Classification with Convolutional Neural Networks"
  (https://arxiv.org/pdf/1812.01187.pdf).
  
  Args:
      layers (int, optional): The layers of ResNet_vd. The supported layers are (18, 34, 50, 101, 152, 200). Default: 50.
      output_stride (int, optional): The stride of output features compared to input images. It is 8 or 16. Default: 8.
      multi_grid (tuple|list, optional): The grid of stage4. Defult: (1, 1, 1).
      pretrained (str, optional): The path of pretrained model.
code: def forward(self, inputs: paddle.Tensor) -> List[paddle.Tensor]:
      y = self.conv1_1(inputs)
      y = self.conv1_2(y)
      y = self.conv1_3(y)
      self.conv1_logit = y.clone()
      y = self.pool2d_max(y)
  
      # A feature list saves the output feature map of each stage.
      feat_list = []
      for stage in self.stage_list:
          for block in stage:
              y = block(y)
          feat_list.append(y)
  
      return feat_list
  
imports: from typing import Union, List, Tuple
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import ann_resnet50_cityscapes.layers
label_desc: Use image node_id 2 for calling the ResNet_vd obj's underlying member method code with example usage: obj.forward(inputs) and returns: feat_list