code: def main(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)
  
file_path: magenta/magenta/models/gansynth/gansynth_generate.py
imports: import os
  import absl.flags
  from magenta.models.gansynth.lib import flags
  from magenta.models.gansynth.lib import generate_util
  from magenta.models.gansynth.lib import model
  from magenta.models.gansynth.lib import util
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: main(unused_argv) without return types