class_docstr: A data pipeline for transform the entire dataset.
  
  # Arguments
      inputs: A list of lists of Preprocessors. For the input datasets for
          the model.
      outputs: A list of lists of Preprocessors. For the target datasets for
          the model.
class_docstr_tok: ["A","data","pipeline","for","transform","the","entire","dataset",".","#","Arguments","inputs",":","A","list","of","lists","of","Preprocessors",".","For","the","input","datasets","for","the","model",".","outputs",":","A","list","of","lists","of","Preprocessors",".","For","the","target","datasets","for","the","model","."]
docstr: Fit the Preprocessors.
docstr_tok: ["Fit","the","Preprocessors","."]
code: def fit(self, dataset):
      """Fit the Preprocessors."""
      x = dataset.map(lambda x, y: x)
      sources_x = data_utils.unzip_dataset(x)
      for pps_list, data in zip(self.inputs, sources_x):
          for preprocessor in pps_list:
              preprocessor.fit(data)
              data = preprocessor.transform(data)
      y = dataset.map(lambda x, y: y)
      sources_y = data_utils.unzip_dataset(y)
      for pps_list, data in zip(self.outputs, sources_y):
          for preprocessor in pps_list:
              preprocessor.fit(data)
              data = preprocessor.transform(data)
      return
  
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 2 for calling the Pipeline obj's underlying member method code with example usage: obj.fit(dataset) without return types