class_docstr: chains two Preprocessing together. The output type of the first
  Preprocessing should be the same with the input type of the second Preprocessing.
class_docstr_tok: ["chains","two","Preprocessing","together",".","The","output","type","of","the","first","Preprocessing","should","be","the","same","with","the","input","type","of","the","second","Preprocessing","."]
code: def __init__(self, transformers, bigdl_type="float"):
      for transfomer in transformers:
          assert isinstance(transfomer, Preprocessing), (
              str(transfomer) + " should be subclass of Preprocessing "
          )
  
      super(ChainedPreprocessing, self).__init__(
          bigdl_type, transformers
      )
  
imports: from bigdl.util.common import *
  from zoo.common.utils import callZooFunc
  from bigdl.dataset.dataset import DataSet
  from pyspark.serializers import CloudPickleSerializer
  import sys
  import math
  import warnings
label_desc: Use image node_id 1 to create a new ChainedPreprocessing object from inherited base classes: Preprocessing with example: obj = ChainedPreprocessing(transformers, bigdl_type)