code: def test_get_best_trials_multi_tuners(self):
      # Instantiate tuner_1
      tuner_1 = self._tuner(
          objective=oracle_module.Objective("val_acc", "max"),
          hyperparameters=self._test_hyperparameters,
          study_config=None,
      )
      tuner_1.tuner_id = "tuner_1"
      # tuner_1 has a completed trial
      trial_1 = trial_module.Trial(
          hyperparameters=self._test_hyperparameters,
          trial_id="1",
          status=trial_module.TrialStatus.COMPLETED,
      )
      tuner_1.oracle.trials = {"1": trial_1}
  
      # Instantiate tuner_2
      tuner_2 = self._tuner(
          objective=oracle_module.Objective("val_acc", "max"),
          hyperparameters=self._test_hyperparameters,
          study_config=None,
      )
      tuner_2.tuner_id = "tuner_2"
      # tuner_2 has a completed trial
      trial_2 = trial_module.Trial(
          hyperparameters=self._test_hyperparameters,
          trial_id="2",
          status=trial_module.TrialStatus.COMPLETED,
      )
      tuner_2.oracle.trials = {"2": trial_2}
  
      self.mock_client.list_trials.return_value = [
          {
              "name": "1",
              "state": "COMPLETED",
              "parameters": [
                  {"parameter": "learning_rate", "floatValue": 0.01}
              ],
              "finalMeasurement": {
                  "stepCount": "3",
                  "metrics": [{"metric": "val_acc", "value": 0.7}],
              },
              "trial_infeasible": False,
              "infeasible_reason": None,
          },
          {
              "name": "2",
              "state": "COMPLETED",
              "parameters": [
                  {"parameter": "learning_rate", "floatValue": 0.001}
              ],
              "finalMeasurement": {
                  "stepCount": "3",
                  "metrics": [{"metric": "val_acc", "value": 0.9}],
              },
              "trial_infeasible": False,
              "infeasible_reason": None,
          },
      ]
  
      # For any tuner worker who tries to get the best trials, all the top N
      # sorted trials will be returned.
      best_trials_1 = tuner_1.oracle.get_best_trials(num_trials=2)
      self.mock_client.list_trials.assert_called_once()
  
      best_trials_2 = tuner_2.oracle.get_best_trials(num_trials=2)
  
      self.assertEqual(len(best_trials_1), 2)
      self.assertEqual(
          best_trials_1[0].trial_id, best_trials_2[0].trial_id
      )
      self.assertEqual(
          best_trials_1[1].trial_id, best_trials_2[1].trial_id
      )
      self.assertEqual(best_trials_1[0].score, 0.9)
      self.assertEqual(best_trials_1[0].best_step, 3)
  
imports: import os
  import time
  from keras_tuner.engine import hypermodel
  from keras_tuner.engine import hyperparameters
  from keras_tuner.engine import oracle
  from keras_tuner.engine import trial
  from keras_tuner.engine import tuner
  import mock
  import tensorflow
  from tensorboard.plugins.hparams import api
  from tensorflow_cloud.core import deploy
  from tensorflow_cloud.core import machine_config
  from tensorflow_cloud.core import validate
  from tensorflow_cloud.tuner import cloud_fit_client
  from tensorflow_cloud.tuner import tuner
  from tensorflow_cloud.tuner.tuner import vizier_client
  from tensorflow_cloud.utils import google_api_client
  from tensorflow_cloud.utils import tf_utils
label_desc: Use image node_id 22 for calling the CloudTunerTest obj's underlying member method code with example usage: obj.test_get_best_trials_multi_tuners() without return types