How to use the climpred.alignment.return_inits_and_verif_dates function in climpred

result (xarray object):
            Verification metric over ``lead`` reduced by dimension(s) ``dim``.
    """
    metric, comparison, dim = _get_metric_comparison_dim(
        metric, comparison, dim, kind='hindcast'
    )
    hind = convert_time_index(hind, 'init', 'hind[init]')
    verif = convert_time_index(verif, 'time', 'verif[time]')
    has_valid_lead_units(hind)

    forecast, verif = comparison.function(hind, verif, metric=metric)

    # think in real time dimension: real time = init + lag
    forecast = forecast.rename({'init': 'time'})

    inits, verif_dates = return_inits_and_verif_dates(
        forecast, verif, alignment=alignment
    )

    log_compute_hindcast_header(metric, comparison, dim, alignment)

    metric_over_leads = [
        _apply_metric_at_given_lead(
            verif,
            verif_dates,
            lead,
            hind=forecast,
            inits=inits,
            metric=metric,
            comparison=comparison,
            dim=dim,
            **metric_kwargs,

'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)
    # temporarily change `init` to `time` for comparison to verification data time.
    hind = hind.rename({'init': 'time'})

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

    plag = []
    for i in hind.lead.values:
        a = verif.sel(time=inits[i])
        b = verif.sel(time=verif_dates[i])
        a['time'] = b['time']
        plag.append(
            metric.function(a, b, dim='time', comparison=__e2c, **metric_kwargs)
        )
    pers = xr.concat(plag, 'lead')
    pers['lead'] = hind.lead.values
    # keep coords from hind
    drop_dims = [d for d in hind.coords if d in CLIMPRED_DIMS]
    if 'iteration' in hind.dims:
        drop_dims += ['iteration']
    pers = copy_coords_from_to(hind.drop_vars(drop_dims), pers)

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)
        a['time'] = b['time']
        plag.append(
            metric.function(a, b, dim='time', comparison=comparison, **metric_kwargs)
        )
    uninit_skill = xr.concat(plag, 'lead')

verif,
            hist,
            reference,
            metric,
            comparison,
            alignment,
            dim,
            **metric_kwargs,
        ):
            """Interior verify func to be passed to apply func."""
            metric, comparison, dim = _get_metric_comparison_dim(
                metric, comparison, dim, kind=self.kind
            )
            forecast, verif = comparison.function(hind, verif, metric=metric)
            forecast = forecast.rename({'init': 'time'})
            inits, verif_dates = return_inits_and_verif_dates(
                forecast, verif, alignment, reference=reference, hist=hist,
            )
            metric_over_leads = [
                _apply_metric_at_given_lead(
                    verif,
                    verif_dates,
                    lead,
                    hind=forecast,
                    hist=hist,
                    inits=inits,
                    # Ensure apply metric function returns skill and not reference
                    # results.
                    reference=None,
                    metric=metric,
                    comparison=comparison,
                    dim=dim,