class_docstr: Adds special handling for K8s executor config.
  
  If we unpickle a k8s object that was pickled under an earlier k8s library version, then
  the unpickled object may throw an error when to_dict is called.  To be more tolerant of
  version changes we convert to JSON using Airflow's serializer before pickling.
class_docstr_tok: ["Adds","special","handling","for","K8s","executor","config",".","If","we","unpickle","a","k8s","object","that","was","pickled","under","an","earlier","k8s","library","version",",","then","the","unpickled","object","may","throw","an","error","when","to_dict","is","called",".","To","be","more","tolerant","of","version","changes","we","convert","to","JSON","using","Airflow","'s","serializer","before","pickling","."]
code: def result_processor(self, dialect, coltype):
      from airflow.serialization.serialized_objects import (
          BaseSerialization,
      )
  
      super_process = super().result_processor(dialect, coltype)
  
      def process(value):
          value = super_process(value)  # unpickle
  
          if isinstance(value, dict) and "pod_override" in value:
              pod_override = value["pod_override"]
  
              if isinstance(pod_override, dict) and pod_override.get(
                  Encoding.TYPE
              ):
                  # If pod_override was serialized with Airflow's BaseSerialization, deserialize it
                  value["pod_override"] = BaseSerialization.deserialize(
                      pod_override
                  )
              else:
                  # backcompat path
                  # we no longer pickle raw pods but this code may be reached
                  # when accessing executor configs created in a prior version
                  new_pod = ensure_pod_is_valid_after_unpickling(
                      pod_override
                  )
                  if new_pod:
                      value["pod_override"] = new_pod
          return value
  
      return process
  
imports: from __future__ import annotations
  import contextlib
  import copy
  import datetime
  import json
  import logging
  from typing import TYPE_CHECKING, Any, Generator, Iterable, overload
  from dateutil import relativedelta
  from sqlalchemy import TIMESTAMP, PickleType, event, nullsfirst, tuple_
  from sqlalchemy.dialects import mysql
  from sqlalchemy.types import JSON, Text, TypeDecorator
  from airflow.configuration import conf
  from airflow.serialization.enums import Encoding
  from airflow.utils.timezone import make_naive, utc
label_desc: Use image node_id 2 for calling the ExecutorConfigType obj's underlying member method code with example usage: obj.result_processor(dialect, coltype) and returns: process, value