code: def hparams(hparams, trial_id=None, start_time_secs=None):
      # NOTE: Keep docs in sync with `hparams_pb` below.
      """Write hyperparameter values for a single trial.
  
      Args:
        hparams: A `dict` mapping hyperparameters to the values used in this
          trial. Keys should be the names of `HParam` objects used in an
          experiment, or the `HParam` objects themselves. Values should be
          Python `bool`, `int`, `float`, or `string` values, depending on
          the type of the hyperparameter. The corresponding numpy types,
          like `np.float32`, are also permitted.
        trial_id: An optional `str` ID for the set of hyperparameter values
          used in this trial. Defaults to a hash of the hyperparameters.
        start_time_secs: The time that this trial started training, as
          seconds since epoch. Defaults to the current time.
  
      Returns:
        A tensor whose value is `True` on success, or `False` if no summary
        was written because no default summary writer was available.
      """
      pb = hparams_pb(
          hparams=hparams,
          trial_id=trial_id,
          start_time_secs=start_time_secs,
      )
      return _write_summary("hparams", pb)
  
imports: import abc
  import hashlib
  import json
  import random
  import time
  import numpy
  from tensorboard.compat import tf2
  from tensorboard.compat.proto import summary_pb2
  from tensorboard.plugins.hparams import api_pb2
  from tensorboard.plugins.hparams import metadata
  from tensorboard.plugins.hparams import plugin_data_pb2
label_desc: Use image node_id 1 for calling a global function with example usage: hparams(hparams, trial_id, start_time_secs) and returns: _write_summary