code: def main():
      global_config = config["Global"]
  
      # build post process
      post_process_class = build_post_process(
          config["PostProcess"], global_config
      )
  
      # build model
      model = build_model(config["Architecture"])
  
      load_model(config, model)
  
      # create data ops
      transforms = []
      for op in config["Eval"]["dataset"]["transforms"]:
          op_name = list(op)[0]
          if "Label" in op_name:
              continue
          elif op_name == "KeepKeys":
              op[op_name]["keep_keys"] = ["image"]
          elif op_name == "SSLRotateResize":
              op[op_name]["mode"] = "test"
          transforms.append(op)
      global_config["infer_mode"] = True
      ops = create_operators(transforms, global_config)
  
      model.eval()
      for file in get_image_file_list(config["Global"]["infer_img"]):
          logger.info("infer_img: {}".format(file))
          with open(file, "rb") as f:
              img = f.read()
              data = {"image": img}
          batch = transform(data, ops)
  
          images = np.expand_dims(batch[0], axis=0)
          images = paddle.to_tensor(images)
          preds = model(images)
          post_result = post_process_class(preds)
          for rec_result in post_result:
              logger.info("\t result: {}".format(rec_result))
      logger.info("success!")
  
file_path: PaddleOCR/tools/infer_cls.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import os
  import sys
  import paddle
  from ppocr.data import create_operators, transform
  from ppocr.modeling.architectures import build_model
  from ppocr.postprocess import build_post_process
  from ppocr.utils.save_load import load_model
  from ppocr.utils.utility import get_image_file_list
  import tools.program
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types