class_docstr: A set of data which is used in the model optimization process. The FeatureSet can be accessed in
  a random data sample sequence. In the training process, the data sequence is a looped endless
  sequence. While in the validation process, the data sequence is a limited length sequence.
  Different from BigDL's DataSet, this FeatureSet could be cached to Intel Optane DC Persistent
  Memory, if you set memory_type to PMEM when creating FeatureSet.
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docstr: Helper function to transform the data type in the data set.
  :param transformer: the transformers to transform this feature set.
  :return: A feature set
docstr_tok: ["Helper","function","to","transform","the","data","type","in","the","data","set",".",":","param","transformer",":","the","transformers","to","transform","this","feature","set",".",":","return",":","A","feature","set"]
code: def transform(self, transformer):
      """
      Helper function to transform the data type in the data set.
      :param transformer: the transformers to transform this feature set.
      :return: A feature set
      """
      jvalue = callZooFunc(
          self.bigdl_type,
          "transformFeatureSet",
          self.value,
          transformer,
      )
      return FeatureSet(jvalue=jvalue)
  
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 8 for calling the FeatureSet obj's underlying member method code with example usage: obj.transform(transformer) and returns: FeatureSet