code: def run_all(logdir, verbose=False):
      """Perform random search over the hyperparameter space.
  
      Arguments:
        logdir: The top-level directory into which to write data. This
          directory should be empty or nonexistent.
        verbose: If true, print out each run's name as it begins.
      """
      data = prepare_data()
      rng = random.Random(0)
  
      with tf.summary.create_file_writer(logdir).as_default():
          hp.hparams_config(hparams=HPARAMS, metrics=METRICS)
  
      sessions_per_group = 2
      num_sessions = flags.FLAGS.num_session_groups * sessions_per_group
      session_index = 0  # across all session groups
      for group_index in range(flags.FLAGS.num_session_groups):
          hparams = {h: h.domain.sample_uniform(rng) for h in HPARAMS}
          hparams_string = str(hparams)
          for repeat_index in range(sessions_per_group):
              session_id = str(session_index)
              session_index += 1
              if verbose:
                  print(
                      "--- Running training session %d/%d"
                      % (session_index, num_sessions)
                  )
                  print(hparams_string)
                  print("--- repeat #: %d" % (repeat_index + 1))
              run(
                  data=data,
                  base_logdir=logdir,
                  session_id=session_id,
                  hparams=hparams,
              )
  
file_path: tensorboard/tensorboard/plugins/hparams/hparams_demo.py
imports: import os.path
  import random
  import shutil
  from absl import app
  from absl import flags
  import numpy
  import tensorflow
  from tensorboard.plugins.hparams import api
label_desc: Use image node_id 4 for calling a global function with example usage: run_all(logdir, verbose) without return types