code: def stop(self) -> None:
      # Whereas the regular PySpark session immediately terminates the Spark Context
      # itself, meaning that stopping all Spark sessions, this will only stop this one session
      # on the server.
      # It is controversial to follow the existing the regular Spark session's behavior
      # specifically in Spark Connect the Spark Connect server is designed for
      # multi-tenancy - the remote client side cannot just stop the server and stop
      # other remote clients being used from other users.
      with SparkSession._lock:
          if not self.is_stopped and self.release_session_on_close:
              self.client.release_session()
          self.client.close()
          if self is SparkSession._default_session:
              SparkSession._default_session = None
          if self is getattr(
              SparkSession._active_session, "session", None
          ):
              SparkSession._active_session.session = None
  
          if "SPARK_LOCAL_REMOTE" in os.environ:
              # When local mode is in use, follow the regular Spark session's
              # behavior by terminating the Spark Connect server,
              # meaning that you can stop local mode, and restart the Spark Connect
              # client with a different remote address.
              if PySparkSession._activeSession is not None:
                  PySparkSession._activeSession.stop()
              del os.environ["SPARK_LOCAL_REMOTE"]
              del os.environ["SPARK_CONNECT_MODE_ENABLED"]
              if "SPARK_REMOTE" in os.environ:
                  del os.environ["SPARK_REMOTE"]
  
imports: from pyspark.sql.connect.utils import check_dependencies
  import threading
  import os
  import warnings
  from collections.abc import Sized
  from functools import reduce
  from threading import RLock
  from typing import Optional, Any, Union, Dict, List, Tuple, Set, cast, overload, Iterable, TYPE_CHECKING, ClassVar
  import numpy
  import pandas
  import pyarrow
  from pandas.api.types import is_datetime64_dtype, is_timedelta64_dtype
  import urllib
  from pyspark import SparkContext, SparkConf, __version__
  from pyspark.loose_version import LooseVersion
  from pyspark.sql.connect.client import SparkConnectClient, ChannelBuilder
  from pyspark.sql.connect.conf import RuntimeConf
  from pyspark.sql.connect.dataframe import DataFrame
  from pyspark.sql.connect.plan import SQL, Range, LocalRelation, LogicalPlan, CachedLocalRelation, CachedRelation, CachedRemoteRelation
  from pyspark.sql.connect.readwriter import DataFrameReader
  from pyspark.sql.connect.streaming.readwriter import DataStreamReader
  from pyspark.sql.connect.streaming.query import StreamingQueryManager
  from pyspark.sql.pandas.serializers import ArrowStreamPandasSerializer
  from pyspark.sql.pandas.types import to_arrow_schema, to_arrow_type, _deduplicate_field_names
  from pyspark.sql.session import classproperty, SparkSession
  from pyspark.sql.types import _infer_schema, _has_nulltype, _merge_type, Row, DataType, DayTimeIntervalType, StructType, AtomicType, TimestampType
  from pyspark.sql.utils import to_str
  from pyspark.errors import PySparkAttributeError, PySparkNotImplementedError, PySparkRuntimeError, PySparkValueError, PySparkTypeError
label_desc: Use image node_id 22 for calling the SparkSession obj's underlying member method code with example usage: obj.stop() without return types