code: def __init__(
      self, label_list: list = None, load_checkpoint: str = None
  ):
      super(RepVGG_B1, self).__init__()
  
      if label_list is not None:
          self.labels = label_list
          class_dim = len(self.labels)
      else:
          label_list = []
          label_file = os.path.join(self.directory, "label_list.txt")
          files = open(label_file)
          for line in files.readlines():
              line = line.strip("\n")
              label_list.append(line)
          self.labels = label_list
          class_dim = len(self.labels)
  
      num_blocks = [4, 6, 16, 1]
      width_multiplier = [2, 2, 2, 4]
      self.override_groups_map = dict()
  
      assert 0 not in self.override_groups_map
  
      self.in_planes = min(64, int(64 * width_multiplier[0]))
  
      self.stage0 = RepVGGBlock(
          in_channels=3,
          out_channels=self.in_planes,
          kernel_size=3,
          stride=2,
          padding=1,
      )
      self.cur_layer_idx = 1
      self.stage1 = self._make_stage(
          int(64 * width_multiplier[0]), num_blocks[0], stride=2
      )
      self.stage2 = self._make_stage(
          int(128 * width_multiplier[1]), num_blocks[1], stride=2
      )
      self.stage3 = self._make_stage(
          int(256 * width_multiplier[2]), num_blocks[2], stride=2
      )
      self.stage4 = self._make_stage(
          int(512 * width_multiplier[3]), num_blocks[3], stride=2
      )
      self.gap = nn.AdaptiveAvgPool2D(output_size=1)
      self.linear = nn.Linear(int(512 * width_multiplier[3]), class_dim)
  
      if load_checkpoint is not None:
          self.model_dict = paddle.load(load_checkpoint)
          self.set_dict(self.model_dict)
          print("load custom checkpoint success")
      else:
          checkpoint = os.path.join(self.directory, "model.pdparams")
          self.model_dict = paddle.load(checkpoint)
          self.set_dict(self.model_dict)
          print("load pretrained checkpoint success")
  
imports: import os
  from typing import Union
  import paddle
  import paddle.nn
  import paddlehub.vision.transforms
  import numpy
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
label_desc: Use image node_id 1 to create a new RepVGG_B1 object from inherited base classes: nn with example: obj = RepVGG_B1(label_list, load_checkpoint)