code: def maybe_convert_dtype(data, copy):
      
      if is_float_dtype(data):
          # Note: we must cast to datetime64[ns] here in order to treat these
          #  as wall-times instead of UTC timestamps.
          data = data.astype(_NS_DTYPE)
          copy = False
          # TODO: deprecate this behavior to instead treat symmetrically
          #  with integer dtypes.  See discussion in GH#23675
  
      elif is_timedelta64_dtype(data):
          warnings.warn("Passing timedelta64-dtype data is deprecated, will "
                        "raise a TypeError in a future version",
                        FutureWarning, stacklevel=5)
          data = data.view(_NS_DTYPE)
  
      elif is_period_dtype(data):
          # Note: without explicitly raising here, PeriodIndex
          #  test_setops.test_join_does_not_recur fails
          raise TypeError("Passing PeriodDtype data is invalid.  "
                          "Use `data.to_timestamp()` instead")
  
      elif is_categorical_dtype(data):
          # GH#18664 preserve tz in going DTI->Categorical->DTI
          # TODO: cases where we need to do another pass through this func,
          #  e.g. the categories are timedelta64s
          data = data.categories.take(data.codes, fill_value=NaT)._values
          copy = False
  
      elif is_extension_type(data) and not is_datetime64tz_dtype(data):
          # Includes categorical
          # TODO: We have no tests for these
          data = np.array(data, dtype=np.object_)
          copy = False
  
      return data, copy
docs: Convert data based on dtype conventions, issuing deprecation warnings
      or errors where appropriate.
  
      Parameters
      ----------
      data : np.ndarray or pd.Index
      copy : bool
  
      Returns
      -------
      data : np.ndarray or pd.Index
      copy : bool
  
      Raises
      ------
      TypeError : PeriodDType data is passed