code: def main(output_path):
      tf.reset_default_graph()
      with tf.Graph().as_default():
          expected_box_encodings = tf.placeholder(
              dtype=tf.float32, shape=[1, 564, 16]
          )
          expected_scores = tf.placeholder(
              dtype=tf.float32, shape=[1, 564, 1]
          )
          actual_box_encodings = tf.placeholder(
              dtype=tf.float32, shape=[1, 564, 16]
          )
          actual_scores = tf.placeholder(
              dtype=tf.float32, shape=[1, 564, 1]
          )
  
          [kld_metric, box_mse, ok] = metrics(
              expected_box_encodings,
              expected_scores,
              actual_box_encodings,
              actual_scores,
          )
          ok = tf.reshape(ok, [1], name="ok")
          kld_metric = tf.reshape(
              kld_metric, [1], name="symmetric_kl_divergence"
          )
          box_mse = tf.reshape(box_mse, [1], name="box_mse")
          sess = tf.compat.v1.Session()
          converter = tf.lite.TFLiteConverter.from_session(
              sess,
              [
                  expected_box_encodings,
                  expected_scores,
                  actual_box_encodings,
                  actual_scores,
              ],
              [kld_metric, box_mse, ok],
          )
          converter.experimental_new_converter = True
          tflite_model = converter.convert()
          if sys.byteorder == "big":
              tflite_model = flatbuffer_utils.byte_swap_tflite_buffer(
                  tflite_model, "big", "little"
              )
          open(output_path, "wb").write(tflite_model)
  
imports: import argparse
  import sys
  import tensorflow.compat.v1
  from tensorflow.lite.experimental.acceleration.mini_benchmark.metrics import kl_divergence
  from tensorflow.lite.tools import flatbuffer_utils
label_desc: Use image node_id 2 for calling a global function with example usage: main(output_path) without return types