code: def main(unused_argv):
      model_path = train_util.expand_path(FLAGS.model_path)
  
      # Figure out what type the model path is.
      is_saved_model = tf.io.gfile.exists(
          os.path.join(model_path, "saved_model.pb")
      )
      is_ckpt = not tf.io.gfile.isdir(model_path)
  
      # Infer save directory path.
      export_name = FLAGS.name if FLAGS.name else "export"
      if FLAGS.save_dir:
          save_dir = FLAGS.save_dir
      else:
          if is_saved_model:
              # If model_path is a SavedModel, use that directory.
              save_dir = model_path
          elif is_ckpt:
              # If model_path is a checkpoint file, use the directory of the file.
              save_dir = os.path.join(
                  os.path.dirname(model_path), export_name
              )
          else:
              # If model_path is a checkpoint directory, use child export directory.
              save_dir = os.path.join(model_path, export_name)
  
      # Make a new save directory.
      save_dir = train_util.expand_path(save_dir)
      ensure_exits(save_dir)
  
      # Save reverb impulse response.
      if FLAGS.reverb:
          export_impulse_response(
              model_path, save_dir, FLAGS.reverb_sample_rate
          )
  
      # Save metadata.
      if FLAGS.metadata:
          metadata_path = os.path.join(save_dir, "metadata.json")
          data_provider = get_data_provider(
              FLAGS.dataset_path, model_path
          )
          metadata = get_metadata_dict(data_provider, model_path)
          with tf.io.gfile.GFile(metadata_path, "w") as f:
              f.write(json.dumps(metadata))
  
      # Create SavedModel if none already exists.
      if not is_saved_model:
          ckpt_to_saved_model(model_path, save_dir)
  
      # Convert SavedModel.
      if FLAGS.tfjs:
          tfjs_dir = os.path.join(save_dir, "tfjs")
          ensure_exits(tfjs_dir)
          saved_model_to_tfjs(save_dir, tfjs_dir)
  
      if FLAGS.tflite:
          tflite_dir = os.path.join(save_dir, "tflite")
          ensure_exits(tflite_dir)
          saved_model_to_tflite(
              save_dir,
              tflite_dir,
              metadata_path if FLAGS.metadata else "",
              name=FLAGS.name,
          )
  
imports: import datetime
  import json
  import os
  from absl import app
  from absl import flags
  import ddsp
  from ddsp.training import data
  from ddsp.training import inference
  from ddsp.training import postprocessing
  from ddsp.training import train_util
  import gin
  import librosa
  import note_seq
  import tensorflow
  from tensorflowjs.converters import converter
  from tflite_support import metadata
label_desc: Use image node_id 9 for calling a global function with example usage: main(unused_argv) without return types