How to use the climpred.metrics.METRIC_ALIASES function in climpred

Reference:
        * Chapter 8 (Short-Term Climate Prediction) in Van den Dool, Huug.
          Empirical methods in short-term climate prediction.
          Oxford University Press, 2007.

    """
    # Check that init is int, cftime, or datetime; convert ints or cftime to datetime.
    hind = convert_time_index(hind, 'init', 'hind[init]')
    verif = convert_time_index(verif, 'time', 'verif[time]')
    # Put this after `convert_time_index` since it assigns 'years' attribute if the
    # `init` dimension is a `float` or `int`.
    has_valid_lead_units(hind)

    # get metric/comparison function name, not the alias
    metric = METRIC_ALIASES.get(metric, metric)

    # get class metric(Metric)
    metric = get_metric_class(metric, DETERMINISTIC_HINDCAST_METRICS)
    if metric.probabilistic:
        raise ValueError(
            'probabilistic metric ',
            metric.name,
            'cannot compute persistence forecast.',
        )
    # If lead 0, need to make modifications to get proper persistence, since persistence
    # at lead 0 is == 1.
    if [0] in hind.lead.values:
        hind = hind.copy()
        with xr.set_options(keep_attrs=True):  # keeps lead.attrs['units']

            hind['lead'] = hind['lead'] + 1

should be based on the same set of verification dates.
        add_attrs (bool): write climpred compute args to attrs. default: True
        ** metric_kwargs (dict): additional keywords to be passed to metric

    Returns:
        u (xarray object): Results from comparison at the first lag.

    """
    # Check that init is int, cftime, or datetime; convert ints or cftime to datetime.
    hind = convert_time_index(hind, 'init', 'hind[init]')
    uninit = convert_time_index(uninit, 'time', 'uninit[time]')
    verif = convert_time_index(verif, 'time', 'verif[time]')
    has_valid_lead_units(hind)

    # get metric/comparison function name, not the alias
    metric = METRIC_ALIASES.get(metric, metric)
    comparison = COMPARISON_ALIASES.get(comparison, comparison)

    comparison = get_comparison_class(comparison, HINDCAST_COMPARISONS)
    metric = get_metric_class(metric, DETERMINISTIC_HINDCAST_METRICS)
    forecast, verif = comparison.function(uninit, verif, metric=metric)

    hind = hind.rename({'init': 'time'})

    _, verif_dates = return_inits_and_verif_dates(hind, verif, alignment=alignment)

    plag = []
    # TODO: Refactor this, getting rid of `compute_uninitialized` completely.
    # `same_verifs` does not need to go through the loop, since it's a fixed
    # skill over all leads.
    for i in hind['lead'].values:
        # Ensure that the uninitialized reference has all of the

* compute_perfect_model()
    * compute_hindcast()

    Args:
        metric (str): name of metric.
        list_ (list): check whether metric in list

    Returns:
        metric (Metric): class object of the metric.
    """
    if isinstance(metric, metrics.Metric):
        return metric
    elif isinstance(metric, str):
        # check if metric allowed
        is_in_list(metric, list_, 'metric')
        metric = METRIC_ALIASES.get(metric, metric)
        return getattr(metrics, '__' + metric)
    else:
        raise ValueError(
            f'Please provide metric as str or Metric class, found {type(metric)}'
        )

comparison (Comparison): comparison class.
        dim (list of str or str): corrected dimension to apply metric to.
    """
    # check kind allowed
    is_in_list(kind, ['hindcast', 'PM'], 'kind')
    # set default dim
    if dim is None:
        dim = 'init' if kind == 'hindcast' else ['init', 'member']
    # check allowed dims
    if kind == 'hindcast':
        is_in_list(dim, ['member', 'init'], 'dim')
    elif kind == 'PM':
        is_in_list(dim, ['member', 'init', ['init', 'member']], 'dim')

    # get metric and comparison strings incorporating alias
    metric = METRIC_ALIASES.get(metric, metric)
    comparison = COMPARISON_ALIASES.get(comparison, comparison)

    METRICS = HINDCAST_METRICS if kind == 'hindcast' else PM_METRICS
    COMPARISONS = HINDCAST_COMPARISONS if kind == 'hindcast' else PM_COMPARISONS
    metric = get_metric_class(metric, METRICS)
    comparison = get_comparison_class(comparison, COMPARISONS)

    # check whether combination of metric and comparison works
    PROBABILISTIC_COMPARISONS = (
        PROBABILISTIC_HINDCAST_COMPARISONS
        if kind == 'hindcast'
        else PROBABILISTIC_PM_COMPARISONS
    )
    if metric.probabilistic:
        if not comparison.probabilistic:
            raise ValueError(