class_docstr: Represents a distributed set of elements backed by an RDD,
  which is created by applying tensorflow dataset transformations
  on each partitions.
code: def as_tf_dataset_rdd(self) -> "RDD[Any]":
      create_dataset_fn = self.create_dataset_fn
  
      def to_dataset(iter):
          data_list = list(iter)
          if not data_list:
              return []
  
          from tensorflow.python.distribute.coordinator.values import (
              serialize_dataset_to_graph,
          )
  
          datasets = [create_dataset_fn(data) for data in data_list]
          from functools import reduce
  
          dataset = reduce(lambda x, y: x.concatenate(y), datasets)
          ds_def = serialize_dataset_to_graph(dataset).numpy()
          elem_spec = dataset.element_spec
          return [{"ds_def": ds_def, "elem_spec": elem_spec}]
  
      tf_dataset_rdd = self.xshards.rdd.mapPartitions(to_dataset)
      return tf_dataset_rdd
  
imports: import tensorflow
  from bigdl.orca.tfpark.tf_dataset import TensorMeta
  from bigdl.dllib.utils import nest
  from bigdl.orca.data import SparkXShards
  from bigdl.dllib.utils import log4Error
  from bigdl.dllib.utils.log4Error import invalidInputError
  from typing import TYPE_CHECKING, Callable, Optional, Any
label_desc: Use image node_id 3 for calling the Dataset obj's underlying member method code with example usage: obj.as_tf_dataset_rdd() and returns: tf_dataset_rdd, list, list