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 __init__(self, inputs, outputs, **kwargs):
      super().__init__(**kwargs)
      self.inputs = inputs
      self.outputs = outputs
  
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 1 to create a new Pipeline object from inherited base classes: pps_module with example: obj = Pipeline(inputs, outputs)