code: def convert_hyperparams_to_hparams(
      hyperparams: hp_module.HyperParameters,
  ) -> Dict[hparams_api.HParam, Any]:
      """Converts KerasTuner HyperParameters to TensorBoard HParams.
  
      Args:
          hyperparams: A KerasTuner HyperParameters instance
  
      Returns:
          A dict that maps TensorBoard HParams to current values.
      """
      hparams = {}
      for hp in hyperparams.space:
          hparams_value = {}
          try:
              hparams_value = hyperparams.get(hp.name)
          except ValueError:
              continue
  
          hparams_domain = {}
          if isinstance(hp, hp_module.Choice):
              hparams_domain = hparams_api.Discrete(hp.values)
          elif isinstance(hp, hp_module.Int):
              if hp.step is None or hp.step == 1:
                  hparams_domain = hparams_api.IntInterval(
                      hp.min_value, hp.max_value
                  )
              else:
                  # Note: `hp.max_value` is inclusive, unlike the end index
                  # of Python `range()`, which is exclusive
                  values = list(
                      range(hp.min_value, hp.max_value + 1, hp.step)
                  )
                  hparams_domain = hparams_api.Discrete(values)
          elif isinstance(hp, hp_module.Float):
              if hp.step is None:
                  hparams_domain = hparams_api.RealInterval(
                      hp.min_value, hp.max_value
                  )
              else:
                  # Note: `hp.max_value` is inclusive, which is also
                  # the default for Numpy's linspace
                  num_samples = int(
                      (hp.max_value - hp.min_value) / hp.step
                  )
                  end_value = hp.min_value + (num_samples * hp.step)
                  values = np.linspace(
                      hp.min_value, end_value, num_samples + 1
                  ).tolist()
                  hparams_domain = hparams_api.Discrete(values)
          elif isinstance(hp, hp_module.Boolean):
              hparams_domain = hparams_api.Discrete([True, False])
          elif isinstance(hp, hp_module.Fixed):
              hparams_domain = hparams_api.Discrete([hp.value])
          else:
              raise ValueError(
                  "`HyperParameter` type not recognized: {}".format(hp)
              )
  
          hparams_key = hparams_api.HParam(hp.name, hparams_domain)
          hparams[hparams_key] = hparams_value
  
      return hparams
  
imports: from typing import Any, Dict, List, Optional, Text, Union
  from keras_tuner.engine import hyperparameters
  from keras_tuner.engine import metrics_tracking
  from keras_tuner.engine import oracle
  from keras_tuner.engine import trial
  import numpy
  from tensorboard.plugins.hparams import api
label_desc: Use image node_id 6 for calling a global function with example usage: convert_hyperparams_to_hparams(hyperparams) and returns: hparams