code: def hparams_config(hparams, metrics, time_created_secs=None):
      # NOTE: Keep docs in sync with `hparams_config_pb` below.
      """Write a top-level experiment configuration.
  
      This configuration describes the hyperparameters and metrics that will
      be tracked in the experiment, but does not record any actual values of
      those hyperparameters and metrics. It can be created before any models
      are actually trained.
  
      Args:
        hparams: A list of `HParam` values.
        metrics: A list of `Metric` values.
        time_created_secs: The time that this experiment was created, 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_config_pb(
          hparams=hparams,
          metrics=metrics,
          time_created_secs=time_created_secs,
      )
      return _write_summary("hparams_config", 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 3 for calling a global function with example usage: hparams_config(hparams, metrics, time_created_secs) and returns: _write_summary