code: def benchmark_predict(self):
      test_num = 500
      test_time = 0.0
      for i in range(0, test_num + 10):
          inputs = np.random.rand(args.batch_size, 3, 112, 112).astype(
              np.float32
          )
          start_time = time.time()
          batch_output = self.predict(inputs).flatten()
          if i >= 10:
              test_time += time.time() - start_time
          # time.sleep(0.01)  # sleep for T4 GPU
  
      print(
          "{0}\tbatch size: {1}\ttime(ms): {2}".format(
              args.model, args.batch_size, 1000 * test_time / test_num
          )
      )
  
file_path: insightface/recognition/arcface_paddle/tools/benchmark_speed.py
imports: import os
  import numpy
  import time
  import argparse
  from paddle.inference import Config
  from paddle.inference import create_predictor
label_desc: Use image node_id 3 for calling the Predictor obj's underlying member method code with example usage: obj.benchmark_predict() without return types