code: def main(_):
      interpreter = tf.lite.Interpreter(model_path=flags.model_file)
      interpreter.allocate_tensors()
      num_correct, total = 0, 0
      for input_data in test_image_generator():
          output = run_eval(interpreter, input_data[0])
          total += 1
          if output == input_data[1]:
              num_correct += 1
          if total % 500 == 0:
              print(
                  "Accuracy after %i images: %f"
                  % (total, float(num_correct) / float(total))
              )
  
imports: import numpy
  import tensorflow
  from tensorflow.lite.tutorials import dataset
label_desc: Use image node_id 3 for calling a global function with example usage: main(_) without return types