def METHOD_NAME(unused_argv):
  absl.flags.FLAGS.alsologtostderr = True
  # Load the model
  flags = lib_flags.Flags(
      {
          'batch_size_schedule': [FLAGS.batch_size],
          'tfds_data_dir': FLAGS.tfds_data_dir
      })
  model = lib_model.Model.load_from_path(FLAGS.ckpt_dir, flags)
  # Make an output directory if it doesn't exist
  output_dir = util.expand_path(FLAGS.output_dir)
  if not tf.gfile.Exists(output_dir):
    tf.gfile.MakeDirs(output_dir)
  if FLAGS.midi_file:
    # If a MIDI file is provided, synthesize interpolations across the clip
    unused_ns, notes = gu.load_midi(FLAGS.midi_file)
    # Distribute latent vectors linearly in time
    z_instruments, t_instruments = gu.get_random_instruments(
        model,
        notes['end_times'][-1],
        secs_per_instrument=FLAGS.secs_per_instrument)
    # Get latent vectors for each note
    z_notes = gu.get_z_notes(notes['start_times'], z_instruments, t_instruments)
    # Generate audio for each note
    print('Generating {} samples...'.format(len(z_notes)))
    audio_notes = model.generate_samples_from_z(z_notes, notes['pitches'])
    # Make a single audio clip
    audio_clip = gu.combine_notes(audio_notes,
                                  notes['start_times'],
                                  notes['end_times'],
                                  notes['velocities'])
    # Write the wave files
    fname = os.path.join(output_dir, 'generated_clip.wav')
    gu.save_wav(audio_clip, fname)
  else:
    # Otherwise, just generate a batch of random sounds
    waves = model.generate_samples(FLAGS.batch_size)
    # Write the wave files
    for i in range(len(waves)):
      fname = os.path.join(output_dir, 'generated_{}.wav'.format(i))
      gu.save_wav(waves[i], fname)