code: def join(
      self,
      other: "Table",
      merge_op: Callable[[V, V], V] = lambda x, y: x,
      output_value_serdes_type=None,
  ):
      op = _lifted_reduce_to_serdes(
          merge_op, get_serdes_by_type(self.value_serdes_type)
      )
      return from_rdd(
          self._rdd.join(other._rdd).mapValues(
              lambda x: op(x[0], x[1])
          ),
          key_serdes_type=self.key_serdes_type,
          value_serdes_type=output_value_serdes_type
          or self.value_serdes_type,
          partitioner_type=self.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 3 for calling the Table obj's underlying member method code with example usage: obj.join(other, merge_op, output_value_serdes_type) and returns: from_rdd