code: def _impl_map_reduce_partitions_with_index(
      self,
      map_partition_op: Callable[
          [int, Iterable[Tuple[K, V]]], Iterable
      ],
      reduce_partition_op: Optional[Callable[[Any, Any], Any]],
      shuffle: bool,
      input_key_serdes,
      input_key_serdes_type: int,
      input_value_serdes,
      input_value_serdes_type: int,
      input_partitioner,
      input_partitioner_type: int,
      output_key_serdes,
      output_key_serdes_type: int,
      output_value_serdes,
      output_value_serdes_type: int,
      output_partitioner,
      output_partitioner_type: int,
      output_num_partitions: int,
  ) -> "KVTable":
      rdd = self._rdd.mapPartitionsWithIndex(map_partition_op)
  
      if reduce_partition_op is not None:
          rdd = rdd.reduceByKey(reduce_partition_op)
      return from_rdd(
          rdd=rdd,
          key_serdes_type=output_key_serdes_type,
          value_serdes_type=output_value_serdes_type,
          partitioner_type=output_partitioner_type,
      )
  
imports: import logging
  import struct
  import typing
  from typing import Callable, Iterable, Tuple, Optional, Any
  import pyspark
  from pyspark.rddsampler import RDDSamplerBase
  from fate.arch import URI
  from fate.arch.computing.api import KVTable, ComputingEngine, K, V
  from fate.arch.computing.api._table import _lifted_reduce_to_serdes, get_serdes_by_type
  from ._materialize import materialize, unmaterialize
label_desc: Use image node_id 15 for calling the Table obj's underlying member method code with example usage: obj._impl_map_reduce_partitions_with_index(map_partition_op, reduce_partition_op, shuffle, input_key_serdes, input_key_serdes_type, input_value_serdes, input_value_serdes_type, input_partitioner, input_partitioner_type, output_key_serdes, output_key_serdes_type, output_value_serdes, output_value_serdes_type, output_partitioner, output_partitioner_type, output_num_partitions) and returns: from_rdd