code: def _to_corrected_pandas_type(dt: DataType) -> Optional[Any]:
      """
      When converting Spark SQL records to Pandas `pandas.DataFrame`, the inferred data type
      may be wrong. This method gets the corrected data type for Pandas if that type may be
      inferred incorrectly.
      """
      import numpy as np
  
      if type(dt) == ByteType:
          return np.int8
      elif type(dt) == ShortType:
          return np.int16
      elif type(dt) == IntegerType:
          return np.int32
      elif type(dt) == LongType:
          return np.int64
      elif type(dt) == FloatType:
          return np.float32
      elif type(dt) == DoubleType:
          return np.float64
      elif type(dt) == BooleanType:
          return bool
      elif type(dt) == TimestampType:
          return np.dtype("datetime64[ns]")
      elif type(dt) == TimestampNTZType:
          return np.dtype("datetime64[ns]")
      elif type(dt) == DayTimeIntervalType:
          return np.dtype("timedelta64[ns]")
      else:
          return None
  
imports: import datetime
  import itertools
  from typing import Any, Callable, Iterable, List, Optional, Union, TYPE_CHECKING
  from pyspark.sql.types import cast, BooleanType, ByteType, ShortType, IntegerType, IntegralType, LongType, FloatType, DoubleType, DecimalType, StringType, BinaryType, DateType, TimestampType, TimestampNTZType, DayTimeIntervalType, ArrayType, MapType, StructType, StructField, NullType, DataType, UserDefinedType, _create_row
  from pyspark.errors import PySparkTypeError, UnsupportedOperationException, PySparkValueError
label_desc: Use image node_id 13 for calling a global function with example usage: _to_corrected_pandas_type(dt) and returns: np, np, np, np, np, np, bool, np, np, np, None