code: def main(unused_argv=None):
      os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu_number)
      source_path = utils.shell_path(FLAGS.source_path)
      checkpoint_path = utils.shell_path(FLAGS.checkpoint_path)
      save_path = utils.shell_path(FLAGS.save_path)
      if not save_path:
          raise ValueError("Must specify a save_path.")
      tf.logging.set_verbosity(FLAGS.log)
  
      # Use directory of files
      if tf.gfile.IsDirectory(source_path):
          files = tf.gfile.ListDirectory(source_path)
          file_extensions = [os.path.splitext(f)[1] for f in files]
          if ".wav" in file_extensions:
              file_extension = ".wav"
          elif ".npy" in file_extensions:
              file_extension = ".npy"
          else:
              raise RuntimeError(
                  "Folder must contain .wav or .npy files."
              )
          file_extension = ".npy" if FLAGS.npy_only else file_extension
          files = sorted(
              [
                  os.path.join(source_path, fname)
                  for fname in files
                  if fname.lower().endswith(file_extension)
              ]
          )
      # Use a single file
      elif source_path.lower().endswith((".wav", ".npy")):
          file_extension = os.path.splitext(source_path.lower())[1]
          files = [source_path]
      else:
          raise ValueError(
              "source_path {} must be a folder or file.".format(
                  source_path
              )
          )
  
      # Now synthesize from files one batch at a time
      batch_size = FLAGS.batch_size
      sample_length = FLAGS.sample_length
      n = len(files)
      for start in range(0, n, batch_size):
          end = start + batch_size
          batch_files = files[start:end]
          save_names = [
              os.path.join(
                  save_path,
                  "gen_"
                  + os.path.splitext(os.path.basename(f))[0]
                  + ".wav",
              )
              for f in batch_files
          ]
          # Encode waveforms
          if file_extension == ".wav":
              batch_data = fastgen.load_batch_audio(
                  batch_files, sample_length=sample_length
              )
              encodings = fastgen.encode(
                  batch_data,
                  checkpoint_path,
                  sample_length=sample_length,
              )
          # Or load encodings
          else:
              encodings = fastgen.load_batch_encodings(
                  batch_files, sample_length=sample_length
              )
          # Synthesize multi-gpu
          if FLAGS.gpu_number != 0:
              with tf.device("/device:GPU:%d" % FLAGS.gpu_number):
                  fastgen.synthesize(
                      encodings,
                      save_names,
                      checkpoint_path=checkpoint_path,
                  )
          # Single gpu
          else:
              fastgen.synthesize(
                  encodings, save_names, checkpoint_path=checkpoint_path
              )
  
imports: import os
  from magenta.models.nsynth import utils
  from magenta.models.nsynth.wavenet import fastgen
  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