code: def maybe_promote(dtype, fill_value=np.nan):
      # if we passed an array here, determine the fill value by dtype
      if isinstance(fill_value, np.ndarray):
          if issubclass(
              fill_value.dtype.type, (np.datetime64, np.timedelta64)
          ):
              fill_value = iNaT
          else:
              # we need to change to object type as our
              # fill_value is of object type
              if fill_value.dtype == np.object_:
                  dtype = np.dtype(np.object_)
              fill_value = np.nan
  
      # returns tuple of (dtype, fill_value)
      if issubclass(dtype.type, np.datetime64):
          fill_value = tslibs.Timestamp(fill_value).value
      elif issubclass(dtype.type, np.timedelta64):
          fill_value = tslibs.Timedelta(fill_value).value
      elif is_datetime64tz_dtype(dtype):
          if isna(fill_value):
              fill_value = NaT
      elif is_extension_array_dtype(dtype) and isna(fill_value):
          fill_value = dtype.na_value
      elif is_float(fill_value):
          if issubclass(dtype.type, np.bool_):
              dtype = np.object_
          elif issubclass(dtype.type, np.integer):
              dtype = np.float64
      elif is_bool(fill_value):
          if not issubclass(dtype.type, np.bool_):
              dtype = np.object_
      elif is_integer(fill_value):
          if issubclass(dtype.type, np.bool_):
              dtype = np.object_
          elif issubclass(dtype.type, np.integer):
              # upcast to prevent overflow
              arr = np.asarray(fill_value)
              if arr != arr.astype(dtype):
                  dtype = arr.dtype
      elif is_complex(fill_value):
          if issubclass(dtype.type, np.bool_):
              dtype = np.object_
          elif issubclass(dtype.type, (np.integer, np.floating)):
              dtype = np.complex128
      elif fill_value is None:
          if is_float_dtype(dtype) or is_complex_dtype(dtype):
              fill_value = np.nan
          elif is_integer_dtype(dtype):
              dtype = np.float64
              fill_value = np.nan
          elif is_datetime_or_timedelta_dtype(dtype):
              fill_value = iNaT
          else:
              dtype = np.object_
              fill_value = np.nan
      else:
          dtype = np.object_
  
      # in case we have a string that looked like a number
      if is_extension_array_dtype(dtype):
          pass
      elif is_datetime64tz_dtype(dtype):
          pass
      elif issubclass(np.dtype(dtype).type, string_types):
          dtype = np.object_
  
      return dtype, fill_value
  
imports: from datetime import datetime, timedelta
  import numpy
  from pandas._libs import lib, tslib, tslibs
  from pandas._libs.tslibs import NaT, OutOfBoundsDatetime, Period, iNaT
  from pandas.compat import PY3, string_types, text_type, to_str
  from .common import _INT64_DTYPE, _NS_DTYPE, _POSSIBLY_CAST_DTYPES, _TD_DTYPE, ensure_int8, ensure_int16, ensure_int32, ensure_int64, ensure_object, is_bool, is_bool_dtype, is_categorical_dtype, is_complex, is_complex_dtype, is_datetime64_dtype, is_datetime64_ns_dtype, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_datetimelike, is_dtype_equal, is_extension_array_dtype, is_extension_type, is_float, is_float_dtype, is_integer, is_integer_dtype, is_object_dtype, is_scalar, is_string_dtype, is_timedelta64_dtype, is_timedelta64_ns_dtype, is_unsigned_integer_dtype, pandas_dtype
  from .dtypes import DatetimeTZDtype, ExtensionDtype, PandasExtensionDtype, PeriodDtype
  from .generic import ABCDatetimeArray, ABCDatetimeIndex, ABCPeriodArray, ABCPeriodIndex, ABCSeries
  from .inference import is_list_like
  from .missing import isna, notna
label_desc: Use image node_id 5 for calling a global function with example usage: maybe_promote(dtype, fill_value) and returns: dtype,  fill_value