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 _add_model_checkpoint_callback(self, callbacks, trial_id):
      callbacks.append(
          tf.keras.callbacks.ModelCheckpoint(
              filepath=self._get_model_checkpoint_dir(trial_id),
              save_freq="epoch",
          )
      )
  
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 7 for calling the DistributingCloudTuner obj's underlying member method code with example usage: obj._add_model_checkpoint_callback(callbacks, trial_id) without return types