class_docstr: An AI Platform Training based distributed CloudTuner.
  
  Attributes:
      oracle: Instance of Oracle class.
      hypermodel: Instance of HyperModel class
      directory: The Google Cloud Storage path for logs and checkpoints.
class_docstr_tok: ["An","AI","Platform","Training","based","distributed","CloudTuner",".","Attributes",":","oracle",":","Instance","of","Oracle","class",".","hypermodel",":","Instance","of","HyperModel","class","directory",":","The","Google","Cloud","Storage","path","for","logs","and","checkpoints","."]
code: def save_model(self, trial_id: int, model, step: int = 0):
      # In remote execution models are saved automatically in Google Cloud
      # Storage (GCS) bucket hence no additional actions are needed to save
      # the model.
      pass
  
imports: import collections
  import copy
  import datetime
  import os
  import time
  from typing import Any, Callable, Dict, List, Mapping, Optional, Text, Union
  from keras_tuner.engine import hypermodel
  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
  from keras_tuner.engine import tuner
  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 utils
  from tensorflow_cloud.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 6 for calling the DistributingCloudTuner obj's underlying member method code with example usage: obj.save_model(trial_id, model, step) without return types