How to use the climpred.utils.get_metric_class function in climpred

# 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(
                f'Probabilistic metric `{metric.name}` requires comparison '
                f'accepting multiple members e.g. `{PROBABILISTIC_COMPARISONS}`, '
                f'found `{comparison.name}`.'
            )
        if dim != 'member':

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
        n, freq = get_lead_cftime_shift_args(hind.lead.attrs['units'], 1)
        # Shift backwards shift for lead zero.
        hind['init'] = shift_cftime_index(hind, 'init', -1 * n, freq)

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
        # dates for alignment.
        dates = list(set(forecast['time'].values) & set(verif_dates[i]))
        a = forecast.sel(time=dates)
        b = verif.sel(time=dates)