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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_dtype(data, copy):
 """ 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 """

    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