code: def maybe_convert_to_index_date_type(index, date):
      
      if isinstance(date, str):
          return date
  
      if isinstance(index, pd.DatetimeIndex):
          if isinstance(date, np.datetime64):
              return date
          else:
              return np.datetime64(str(date))
      else:
          date_type = index.date_type
          if isinstance(date, date_type):
              return date
          else:
              if isinstance(date, np.datetime64):
                  # Convert to datetime.date or datetime.datetime object
                  date = date.item()
  
              if isinstance(date, datetime.date):
                  # Convert to a datetime.datetime object
                  date = datetime.datetime.combine(
                      date, datetime.datetime.min.time())
  
              return date_type(date.year, date.month, date.day, date.hour,
                               date.minute, date.second, date.microsecond)
docs: Convert a datetime-like object to the index's date type.
  
      Datetime indexing in xarray can be done using either a pandas
      DatetimeIndex or a CFTimeIndex.  Both support partial-datetime string
      indexing regardless of the calendar type of the underlying data;
      therefore if a string is passed as a date, we return it unchanged.  If a
      datetime-like object is provided, it will be converted to the underlying
      date type of the index.  For a DatetimeIndex that is np.datetime64; for a
      CFTimeIndex that is an object of type cftime.datetime specific to the
      calendar used.
  
      Parameters
      ----------
      index : pd.Index
          Input time index
      date : datetime-like object or str
          Input datetime
  
      Returns
      -------
      date of the type appropriate for the time index of the Dataset