code: def main(infile, outfile):
      faulthandler_log_path = os.environ.get(
          "PYTHON_FAULTHANDLER_DIR", None
      )
      try:
          if faulthandler_log_path:
              faulthandler_log_path = os.path.join(
                  faulthandler_log_path, str(os.getpid())
              )
              faulthandler_log_file = open(faulthandler_log_path, "w")
              faulthandler.enable(file=faulthandler_log_file)
  
          boot_time = time.time()
          split_index = read_int(infile)
          if split_index == -1:  # for unit tests
              sys.exit(-1)
  
          check_python_version(infile)
  
          # read inputs only for a barrier task
          isBarrier = read_bool(infile)
          boundPort = read_int(infile)
          secret = UTF8Deserializer().loads(infile)
  
          memory_limit_mb = int(
              os.environ.get("PYSPARK_EXECUTOR_MEMORY_MB", "-1")
          )
          setup_memory_limits(memory_limit_mb)
  
          # initialize global state
          taskContext = None
          if isBarrier:
              taskContext = BarrierTaskContext._getOrCreate()
              BarrierTaskContext._initialize(boundPort, secret)
              # Set the task context instance here, so we can get it by TaskContext.get for
              # both TaskContext and BarrierTaskContext
              TaskContext._setTaskContext(taskContext)
          else:
              taskContext = TaskContext._getOrCreate()
          # read inputs for TaskContext info
          taskContext._stageId = read_int(infile)
          taskContext._partitionId = read_int(infile)
          taskContext._attemptNumber = read_int(infile)
          taskContext._taskAttemptId = read_long(infile)
          taskContext._cpus = read_int(infile)
          taskContext._resources = {}
          for r in range(read_int(infile)):
              key = utf8_deserializer.loads(infile)
              name = utf8_deserializer.loads(infile)
              addresses = []
              taskContext._resources = {}
              for a in range(read_int(infile)):
                  addresses.append(utf8_deserializer.loads(infile))
              taskContext._resources[key] = ResourceInformation(
                  name, addresses
              )
  
          taskContext._localProperties = dict()
          for i in range(read_int(infile)):
              k = utf8_deserializer.loads(infile)
              v = utf8_deserializer.loads(infile)
              taskContext._localProperties[k] = v
  
          shuffle.MemoryBytesSpilled = 0
          shuffle.DiskBytesSpilled = 0
          _accumulatorRegistry.clear()
  
          setup_spark_files(infile)
          setup_broadcasts(infile)
  
          _accumulatorRegistry.clear()
          eval_type = read_int(infile)
          if eval_type == PythonEvalType.NON_UDF:
              func, profiler, deserializer, serializer = read_command(
                  pickleSer, infile
              )
          elif eval_type in (
              PythonEvalType.SQL_TABLE_UDF,
              PythonEvalType.SQL_ARROW_TABLE_UDF,
          ):
              func, profiler, deserializer, serializer = read_udtf(
                  pickleSer, infile, eval_type
              )
          else:
              func, profiler, deserializer, serializer = read_udfs(
                  pickleSer, infile, eval_type
              )
  
          init_time = time.time()
  
          def process():
              iterator = deserializer.load_stream(infile)
              out_iter = func(split_index, iterator)
              try:
                  serializer.dump_stream(out_iter, outfile)
              finally:
                  if hasattr(out_iter, "close"):
                      out_iter.close()
  
          if profiler:
              profiler.profile(process)
          else:
              process()
  
          # Reset task context to None. This is a guard code to avoid residual context when worker
          # reuse.
          TaskContext._setTaskContext(None)
          BarrierTaskContext._setTaskContext(None)
      except BaseException as e:
          handle_worker_exception(e, outfile)
          sys.exit(-1)
      finally:
          if faulthandler_log_path:
              faulthandler.disable()
              faulthandler_log_file.close()
              os.remove(faulthandler_log_path)
      finish_time = time.time()
      report_times(outfile, boot_time, init_time, finish_time)
      write_long(shuffle.MemoryBytesSpilled, outfile)
      write_long(shuffle.DiskBytesSpilled, outfile)
  
      # Mark the beginning of the accumulators section of the output
      write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
      send_accumulator_updates(outfile)
  
      # check end of stream
      if read_int(infile) == SpecialLengths.END_OF_STREAM:
          write_int(SpecialLengths.END_OF_STREAM, outfile)
      else:
          # write a different value to tell JVM to not reuse this worker
          write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
          sys.exit(-1)
  
imports: import os
  import sys
  import dataclasses
  import time
  import inspect
  import json
  from typing import Any, Callable, Iterable, Iterator, Optional
  import faulthandler
  from pyspark.accumulators import _accumulatorRegistry
  from pyspark.java_gateway import local_connect_and_auth
  from pyspark.taskcontext import BarrierTaskContext, TaskContext
  from pyspark.resource import ResourceInformation
  from pyspark.rdd import PythonEvalType
  from pyspark.serializers import write_int, read_long, read_bool, write_long, read_int, SpecialLengths, UTF8Deserializer, CPickleSerializer, BatchedSerializer
  from pyspark.sql.functions import SkipRestOfInputTableException
  from pyspark.sql.pandas.serializers import ArrowStreamPandasUDFSerializer, ArrowStreamPandasUDTFSerializer, CogroupArrowUDFSerializer, CogroupPandasUDFSerializer, ArrowStreamUDFSerializer, ArrowStreamGroupUDFSerializer, ApplyInPandasWithStateSerializer
  from pyspark.sql.pandas.types import to_arrow_type
  from pyspark.sql.types import ArrayType, BinaryType, DataType, MapType, Row, StringType, StructType, _create_row, _parse_datatype_json_string
  from pyspark.util import fail_on_stopiteration, handle_worker_exception
  from pyspark import shuffle
  from pyspark.errors import PySparkRuntimeError, PySparkTypeError
  from pyspark.worker_util import check_python_version, read_command, pickleSer, send_accumulator_updates, setup_broadcasts, setup_memory_limits, setup_spark_files, utf8_deserializer
label_desc: Use image node_id 24 for calling a global function with example usage: main(infile, outfile) without return types