code: def main(args):
      cfg = get_config(args.config)
      logging.basicConfig(level=logging.NOTSET)
      logging.info(args.model_path)
  
      backbone = get_model(
          cfg.network, dropout=0.0, num_features=cfg.embedding_size
      ).to("cuda")
      val_callback = CallBackVerification(
          1, 0, cfg.val_targets, cfg.ofrecord_path
      )
  
      state_dict = flow.load(args.model_path)
  
      new_parameters = dict()
      for key, value in state_dict.items():
          if "num_batches_tracked" not in key:
              if key == "fc.weight":
                  continue
              new_key = key.replace("backbone.", "")
              new_parameters[new_key] = value
  
      backbone.load_state_dict(new_parameters)
  
      infer_graph = EvalGraph(backbone, cfg)
      val_callback(1000, backbone, infer_graph)
  
file_path: insightface/recognition/arcface_oneflow/val.py
imports: import oneflow
  from utils.utils_callbacks import CallBackVerification
  from backbones import get_model
  from graph import TrainGraph, EvalGraph
  import logging
  import argparse
  from utils.utils_config import get_config
label_desc: Use image node_id 1 for calling a global function with example usage: main(args) without return types