code: def normalize_pandas_result(it):
      import pandas as pd
  
      arrays = []
      for result in it:
          if isinstance(result, (Row, Tuple)):
              arrays.append(
                  pd.concat(
                      [pd.Series([item]) for item in result], axis=1
                  )
              )
          else:
              arrays.append(pd.Series([result]))
      return arrays
  
imports: import datetime
  import threading
  import time
  from collections.abc import Generator
  from functools import partial
  from typing import Any, Tuple, Dict, List
  from pyflink.common import Row
  from pyflink.fn_execution import pickle
  from pyflink.serializers import PickleSerializer
  from pyflink.table import functions
  from pyflink.table.udf import DelegationTableFunction, DelegatingScalarFunction, ImperativeAggregateFunction, PandasAggregateFunctionWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: normalize_pandas_result(it) and returns: arrays