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 from_config(cls, config):
      return cls(
          inputs=[
              [
                  preprocessors_module.deserialize(preprocessor)
                  for preprocessor in preprocessors
              ]
              for preprocessors in config["inputs"]
          ],
          outputs=[
              [
                  preprocessors_module.deserialize(preprocessor)
                  for preprocessor in preprocessors
              ]
              for preprocessors in config["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 9 for calling the Pipeline obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls