code: def set_flags(flags):
      """Set default hyperparameters."""
      # Must be specified externally
      flags.set_if_empty("train_root_dir", "/tmp/gansynth/train")
      flags.set_if_empty(
          "train_data_path", "/tmp/gansynth/nsynth-train.tfrecord"
      )
  
      ### Dataset ###
      flags.set_if_empty("dataset_name", "nsynth_tfds")
      flags.set_if_empty("data_type", "mel")  # linear, phase, mel
      flags.set_if_empty("audio_length", 64000)
      flags.set_if_empty("sample_rate", 16000)
      # specgrams_simple_normalizer, specgrams_freq_normalizer
      flags.set_if_empty(
          "data_normalizer", "specgrams_prespecified_normalizer"
      )
      flags.set_if_empty("normalizer_margin", 0.8)
      flags.set_if_empty("normalizer_num_examples", 1000)
      flags.set_if_empty("mag_normalizer_a", 0.0661371661726)
      flags.set_if_empty("mag_normalizer_b", 0.113718730221)
      flags.set_if_empty("p_normalizer_a", 0.8)
      flags.set_if_empty("p_normalizer_b", 0.0)
  
      ### Losses ###
      # Gradient norm target for wasserstein loss
      flags.set_if_empty("gradient_penalty_target", 1.0)
      flags.set_if_empty("gradient_penalty_weight", 10.0)
      # Additional penalty to keep the scores from drifting too far from zero
      flags.set_if_empty("real_score_penalty_weight", 0.001)
      # Auxiliary loss for conditioning
      flags.set_if_empty("generator_ac_loss_weight", 1.0)
      flags.set_if_empty("discriminator_ac_loss_weight", 1.0)
      # Weight of G&L consistency loss
      flags.set_if_empty("gen_gl_consistency_loss_weight", 0.0)
  
      ### Optimization ###
      flags.set_if_empty(
          "generator_learning_rate", 0.0004
      )  # Learning rate
      flags.set_if_empty(
          "discriminator_learning_rate", 0.0004
      )  # Learning rate
      flags.set_if_empty("adam_beta1", 0.0)  # Adam beta 1
      flags.set_if_empty("adam_beta2", 0.99)  # Adam beta 2
      flags.set_if_empty(
          "fake_batch_size", 16
      )  # The fake image batch size
  
      ### Distributed Training ###
      flags.set_if_empty(
          "master", ""
      )  # Name of the TensorFlow master to use
      # The number of parameter servers. If the value is 0, then the parameters
      # are handled locally by the worker
      flags.set_if_empty("ps_tasks", 0)
      # The Task ID. This value is used when training with multiple workers to
      # identify each worker
      flags.set_if_empty("task", 0)
  
      ### Debugging ###
      flags.set_if_empty("debug_hook", False)
  
      ### -----------  HPARAM Settings for testing eval ###
      ### Progressive Training ###
      # A list of batch sizes for each resolution, if len(batch_size_schedule)
      # < num_resolutions, pad the schedule in the beginning with first batch size
      flags.set_if_empty("batch_size_schedule", [16, 8])
      flags.set_if_empty("stable_stage_num_images", 32)
      flags.set_if_empty("transition_stage_num_images", 32)
      flags.set_if_empty("total_num_images", 320)
      flags.set_if_empty("save_summaries_num_images", 100)
      flags.set_if_empty("train_progressive", True)
      # For fixed-wall-clock-time training, total training time limit in sec.
      # If specified, overrides the iteration limits.
      flags.set_if_empty("train_time_limit", None)
      flags.set_if_empty("train_time_stage_multiplier", 1.0)
  
      ### Architecture ###
      # Choose an architecture function
      flags.set_if_empty("g_fn", "specgram")  # 'specgram'
      flags.set_if_empty("d_fn", "specgram")
      flags.set_if_empty("latent_vector_size", 256)
      flags.set_if_empty("kernel_size", 3)
      flags.set_if_empty("start_height", 4)  # Start specgram height
      flags.set_if_empty("start_width", 8)  # Start specgram width
      flags.set_if_empty("scale_mode", "ALL")  # Scale mode ALL|H
      flags.set_if_empty("scale_base", 2)  # Resolution multiplier
      flags.set_if_empty(
          "num_resolutions", 7
      )  # N progressive resolutions
      flags.set_if_empty("simple_arch", False)
      flags.set_if_empty("to_rgb_activation", "tanh")
      # Number of filters
      flags.set_if_empty("fmap_base", 512)  # Base number of filters
      flags.set_if_empty("fmap_decay", 1.0)
      flags.set_if_empty("fmap_max", 128)
  
imports: import json
  import os
  import time
  from magenta.models.gansynth.lib import data_helpers
  from magenta.models.gansynth.lib import flags
  from magenta.models.gansynth.lib import network_functions
  from magenta.models.gansynth.lib import networks
  from magenta.models.gansynth.lib import train_util
  from magenta.models.gansynth.lib import util
  import numpy
  import tensorflow.compat.v1
  import tensorflow_gan
label_desc: Use image node_id 1 for calling a global function with example usage: set_flags(flags) without return types