code: def run_parameterized_train_and_eval(
      phoenix_instance,
      oracle,
      tuner_id,
      root_dir,
      max_trials,
      data_provider,
      train_steps,
      eval_steps,
      batch_size,
  ):
      """Train, getting parameters from a tuner.
  
      Args:
        phoenix_instance: a phoenix.Phoenix object.
        oracle: a keras_tuner oracle.
        tuner_id: identifier of the tuner (integer).
        root_dir: the root directory to save the models.
        max_trials: the maximal number of trials allowed.
        data_provider: The data provider object.
        train_steps: The number of training steps.
        eval_steps: The number of evaluation steps.
        batch_size: The batch size (integer).
  
      Returns:
        True if the tuner provided a trial to run, False if the tuner
        has run out of trials.
      """
      trial = oracle.create_trial(tuner_id)
  
      trial_dir = get_trial_dir(root_dir, tuner_id)
      my_id = int(os.path.basename(trial_dir))
      if my_id > max_trials:
          return False
  
      hparams = trial.hyperparameters
      phoenix_hparams = aggregate_initial_architecture(hparams.values)
  
      logging.info("Tuner id: %s", tuner_id)
      logging.info("Training with the following hyperparameters: ")
      logging.info(phoenix_hparams)
  
      with _set_model_dir_for_run_config(model_dir=trial_dir):
          evaluation_metrics = run_train_and_eval(
              hparams=hp.HParams(**phoenix_hparams),
              model_dir=trial_dir,
              phoenix_instance=phoenix_instance,
              data_provider=data_provider,
              train_steps=train_steps,
              eval_steps=eval_steps,
              batch_size=batch_size,
          )
  
      oracle.update_trial(
          trial_id=trial.trial_id,
          metrics=evaluation_metrics,
          step=evaluation_metrics["global_step"],
      )
      oracle.end_trial(
          trial.trial_id, keras_tuner.engine.trial.TrialStatus.COMPLETED
      )
      oracle.update_space(trial.hyperparameters)
      # Display needs the updated trial scored by the Oracle.
      # self._display.on_trial_end(self.oracle.get_trial(trial.trial_id))
      oracle.save()
      return True
  
imports: import contextlib
  import copy
  import json
  import os
  import random
  from absl import flags
  from absl import logging
  import keras_tuner
  from model_search import hparam
  from model_search import phoenix
  from model_search import registry
  from model_search.data import data
  from model_search.metadata import ml_metadata_db
  from model_search.proto import phoenix_spec_pb2
  import tensorflow.compat.v2
  from google.protobuf import text_format
label_desc: Use image node_id 8 for calling a global function with example usage: run_parameterized_train_and_eval(phoenix_instance, oracle, tuner_id, root_dir, max_trials, data_provider, train_steps, eval_steps, batch_size) and returns: True, False