class_docstr: construct a Transformer that convert (Feature, Label) tuple to a Sample.
  The returned Transformer is robust for the case label = null, in which the
  Sample is derived from Feature only.
  :param feature_transformer transformer for feature, transform F to Tensor[T]
  :param label_transformer transformer for label, transform L to Tensor[T]
class_docstr_tok: ["construct","a","Transformer","that","convert","(","Feature",",","Label",")","tuple","to","a","Sample",".","The","returned","Transformer","is","robust","for","the","case","label","=","null",",","in","which","the","Sample","is","derived","from","Feature","only",".",":","param","feature_transformer","transformer","for","feature",",","transform","F","to","Tensor","[","T","]",":","param","label_transformer","transformer","for","label",",","transform","L","to","Tensor","[","T","]"]
code: def __init__(
      self, feature_transformer, label_transformer, bigdl_type="float"
  ):
      super(FeatureLabelPreprocessing, self).__init__(
          bigdl_type, feature_transformer, label_transformer
      )
  
imports: from bigdl.dllib.utils.common import *
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.feature.dataset.dataset import DataSet
  from pyspark.serializers import CloudPickleSerializer
  import sys
  import math
  import warnings
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new FeatureLabelPreprocessing object from inherited base classes: Preprocessing with example: obj = FeatureLabelPreprocessing(feature_transformer, label_transformer, bigdl_type)