code: def experiment_pb(
      hparam_infos,
      metric_infos,
      user="",
      description="",
      time_created_secs=None,
  ):
      """Creates a summary that defines a hyperparameter-tuning experiment.
  
      Args:
        hparam_infos: Array of api_pb2.HParamInfo messages. Describes the
            hyperparameters used in the experiment.
        metric_infos: Array of api_pb2.MetricInfo messages. Describes the metrics
            used in the experiment. See the documentation at the top of this file
            for how to populate this.
        user: String. An id for the user running the experiment
        description: String. A description for the experiment. May contain markdown.
        time_created_secs: float. The time the experiment is created in seconds
        since the UNIX epoch. If None uses the current time.
  
      Returns:
        A summary protobuffer containing the experiment definition.
      """
      if time_created_secs is None:
          time_created_secs = time.time()
      experiment = api_pb2.Experiment(
          description=description,
          user=user,
          time_created_secs=time_created_secs,
          hparam_infos=hparam_infos,
          metric_infos=metric_infos,
      )
      return _summary(
          metadata.EXPERIMENT_TAG,
          plugin_data_pb2.HParamsPluginData(experiment=experiment),
      )
  
file_path: tensorboard/tensorboard/plugins/hparams/summary.py
imports: import time
  import tensorflow
  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: experiment_pb(hparam_infos, metric_infos, user, description, time_created_secs) and returns: _summary