class_docstr: A search space consists of HyperPreprocessors.
  
  # Arguments
      inputs: a list of lists of HyperPreprocessors.
      outputs: a list of lists of HyperPreprocessors.
class_docstr_tok: ["A","search","space","consists","of","HyperPreprocessors",".","#","Arguments","inputs",":","a","list","of","lists","of","HyperPreprocessors",".","outputs",":","a","list","of","lists","of","HyperPreprocessors","."]
docstr: Build a Pipeline by Hyperparameters.
  
  # Arguments
      hp: Hyperparameters.
      dataset: tf.data.Dataset.
  
  # Returns
      An instance of Pipeline.
docstr_tok: ["Build","a","Pipeline","by","Hyperparameters",".","#","Arguments","hp",":","Hyperparameters",".","dataset",":","tf.data.Dataset",".","#","Returns","An","instance","of","Pipeline","."]
code: def build(self, hp, dataset):
      """Build a Pipeline by Hyperparameters.
  
      # Arguments
          hp: Hyperparameters.
          dataset: tf.data.Dataset.
  
      # Returns
          An instance of Pipeline.
      """
      x = dataset.map(lambda x, y: x)
      y = dataset.map(lambda x, y: y)
      return Pipeline(
          inputs=self._build_preprocessors(hp, self.inputs, x),
          outputs=self._build_preprocessors(hp, self.outputs, y),
      )
  
imports: import tensorflow
  from tensorflow import keras
  from tensorflow import nest
  from autokeras import preprocessors
  from autokeras.engine import hyper_preprocessor
  from autokeras.engine import preprocessor
  from autokeras.utils import data_utils
  from autokeras.utils import io_utils
label_desc: Use image node_id 3 for calling the HyperPipeline obj's underlying member method code with example usage: obj.build(hp, dataset) and returns: Pipeline