code: def __init__(
      self, label_list: list = None, load_checkpoint: str = None
  ):
      super(SpinalNet_VGG16, 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)
  
      self.backbone = vgg16()
  
      half_in_size = round(512 * 7 * 7 / 2)
      layer_width = 4096
  
      self.half_in_size = half_in_size
  
      self.fc_spinal_layer1 = nn.Sequential(
          nn.Dropout(p=0.5),
          nn.Linear(half_in_size, layer_width),
          nn.BatchNorm1D(layer_width),
          nn.ReLU(),
      )
      self.fc_spinal_layer2 = nn.Sequential(
          nn.Dropout(p=0.5),
          nn.Linear(half_in_size + layer_width, layer_width),
          nn.BatchNorm1D(layer_width),
          nn.ReLU(),
      )
      self.fc_spinal_layer3 = nn.Sequential(
          nn.Dropout(p=0.5),
          nn.Linear(half_in_size + layer_width, layer_width),
          nn.BatchNorm1D(layer_width),
          nn.ReLU(),
      )
      self.fc_spinal_layer4 = nn.Sequential(
          nn.Dropout(p=0.5),
          nn.Linear(half_in_size + layer_width, layer_width),
          nn.BatchNorm1D(layer_width),
          nn.ReLU(),
      )
      self.fc_out = nn.Sequential(
          nn.Dropout(p=0.5), nn.Linear(layer_width * 4, class_dim)
      )
  
      if load_checkpoint is not None:
          self.model_dict = paddle.load(load_checkpoint)[0]
          self.set_dict(self.model_dict)
          print("load custom checkpoint success")
  
      else:
          checkpoint = os.path.join(
              self.directory, "spinalnet_vgg16.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 numpy
  import paddle
  import paddle.nn
  import paddlehub.vision.transforms
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
  import paddle
  from paddle import nn
label_desc: Use image node_id 1 to create a new SpinalNet_VGG16 object from inherited base classes: nn with example: obj = SpinalNet_VGG16(label_list, load_checkpoint)