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","."]
code: def _transform_data(self, dataset, pps_lists):
      sources = data_utils.unzip_dataset(dataset)
      transformed = []
      for pps_list, data in zip(pps_lists, sources):
          for preprocessor in pps_list:
              data = preprocessor.transform(data)
          transformed.append(data)
      if len(transformed) == 1:
          return transformed[0]
      return tf.data.Dataset.zip(tuple(transformed))
  
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 6 for calling the Pipeline obj's underlying member method code with example usage: obj._transform_data(dataset, pps_lists) and returns: tf, transformed