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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code: def __init__(self, jvalue=None, bigdl_type="float"):
      self.bigdl_type = bigdl_type
      if jvalue:
          self.value = jvalue
  
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 FeatureSet object from inherited base classes: DataSet with example: obj = FeatureSet(jvalue, bigdl_type)