code: def create_model(num_classes):
      # resNet50+fpn+retinanet
      # 注意，这里的norm_layer要和训练脚本中保持一致
      backbone = resnet50_fpn_backbone(
          norm_layer=torch.nn.BatchNorm2d,
          returned_layers=[2, 3, 4],
          extra_blocks=LastLevelP6P7(256, 256),
      )
      model = RetinaNet(backbone, num_classes)
  
      return model
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/retinaNet/predict.py
imports: import os
  import time
  import json
  import torch
  from PIL import Image
  import matplotlib.pyplot
  from torchvision import transforms
  from network_files import RetinaNet
  from backbone import resnet50_fpn_backbone, LastLevelP6P7
  from draw_box_utils import draw_objs
label_desc: Use image node_id 1 for calling a global function with example usage: create_model(num_classes) and returns: model