class_docstr: A Tuner class based on KerasTuner for AutoKeras.
  
  Different from KerasTuner's Tuner class. AutoTuner's not only tunes the
  Hypermodel which can be directly built into a Keras model, but also the
  preprocessors. Therefore, a HyperGraph stores the overall search space
  containing both the Preprocessors and Hypermodel. For every trial, the
  HyperGraph builds the PreprocessGraph and KerasGraph with the provided
  HyperParameters.
  
  The AutoTuner uses EarlyStopping for acceleration during the search and
  fully trains the model with full epochs and with both training and
  validation data.  The fully trained model is the best model to be used by
  AutoModel.
  
  # Arguments
      oracle: keras_tuner Oracle.
      hypermodel: keras_tuner HyperModel.
      **kwargs: The args supported by KerasTuner.
code: def best_pipeline_path(self):
      return os.path.join(self.project_dir, "best_pipeline")
  
imports: import collections
  import copy
  import os
  import keras_tuner
  from tensorflow import keras
  from tensorflow import nest
  from tensorflow.keras import callbacks
  from tensorflow.keras.layers.experimental import preprocessing
  from autokeras import pipeline
  from autokeras.utils import data_utils
  from autokeras.utils import utils
label_desc: Use image node_id 17 for calling the AutoTuner obj's underlying member method code with example usage: obj.best_pipeline_path() and returns: os