code: def wrap_pandas_batch_iter_udf(f, return_type):
      arrow_return_type = to_arrow_type(return_type)
      iter_type_label = (
          "pandas.DataFrame"
          if type(return_type) == StructType
          else "pandas.Series"
      )
  
      def verify_result(result):
          if not isinstance(result, Iterator) and not hasattr(
              result, "__iter__"
          ):
              raise PySparkTypeError(
                  error_class="UDF_RETURN_TYPE",
                  message_parameters={
                      "expected": "iterator of {}".format(
                          iter_type_label
                      ),
                      "actual": type(result).__name__,
                  },
              )
          return result
  
      def verify_element(elem):
          import pandas as pd
  
          if not isinstance(
              elem,
              pd.DataFrame
              if type(return_type) == StructType
              else pd.Series,
          ):
              raise PySparkTypeError(
                  error_class="UDF_RETURN_TYPE",
                  message_parameters={
                      "expected": "iterator of {}".format(
                          iter_type_label
                      ),
                      "actual": "iterator of {}".format(
                          type(elem).__name__
                      ),
                  },
              )
  
          verify_pandas_result(
              elem,
              return_type,
              assign_cols_by_name=True,
              truncate_return_schema=True,
          )
  
          return elem
  
      return lambda *iterator: map(
          lambda res: (res, arrow_return_type),
          map(verify_element, verify_result(f(*iterator))),
      )
  
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 6 for calling a global function with example usage: wrap_pandas_batch_iter_udf(f, return_type) and returns: function, result, elem