code: def main():
      args = parse_args()
      model_name = args.backbone
      ori_weight_name = args.tensorflow_weight
      out_name = args.out_weight
  
      repeat_map = {
          0: 3,
          1: 4,
          2: 5,
          3: 6,
          4: 7,
          5: 7,
          6: 8,
          7: 8,
      }
  
      reader = py_checkpoint_reader.NewCheckpointReader(ori_weight_name)
      weights = {
          n: torch.as_tensor(tf2pth(reader.get_tensor(n)))
          for (n, _) in reader.get_variable_to_shape_map().items()
      }
      bifpn_repeats = repeat_map[int(model_name[14])]
      out = convert_key(model_name, bifpn_repeats, weights)
      result = {"state_dict": out}
      torch.save(result, out_name)
  
file_path: mmdetection/projects/EfficientDet/convert_tf_to_pt.py
imports: import argparse
  import numpy
  import torch
  from tensorflow.python.training import py_checkpoint_reader
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types