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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def maybe_convert_to_index_date_type(index, date):
 """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 """

    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)