How to use the climpred.constants.CLIMPRED_DIMS function in climpred

def _check_only_climpred_dims(pe):
    """Warns if dimensions other than `CLIMPRED_DIMS` are in `PredictionEnsemble`."""
    additional_dims = set(pe.get_initialized().dims) - set(CLIMPRED_DIMS)
    if len(additional_dims) != 0:
        raise DimensionError(
            f'{type(pe.__name__)}.plot() does not allow dimensions other '
            f'than {CLIMPRED_DIMS}, found {additional_dims}. '

Rules, Prediction, and Estimation.” Journal of the American
          Statistical Association 102, no. 477 (March 1, 2007): 359–78.
          https://doi.org/10/c6758w.

    Example:
        >>> compute_perfect_model(ds, control, metric='crpss')
        >>> compute_perfect_model(ds, control, metric='crpss', gaussian=False,
                                  cdf_or_dist=scipy.stats.norm, xminimum=-10,
                                  xmaximum=10, tol=1e-6)

    See also:
        * properscoring.crps_ensemble
        * xskillscore.crps_ensemble
    """
    # available climpred dimensions to take mean and std over
    rdim = [tdim for tdim in verif.dims if tdim in CLIMPRED_DIMS]
    mu = verif.mean(rdim)
    sig = verif.std(rdim)

    # checking metric_kwargs, if not found use defaults: gaussian, else crps_quadrature
    if 'gaussian' in metric_kwargs:
        gaussian = metric_kwargs['gaussian']
    else:
        gaussian = True

    if gaussian:
        ref_skill = _crps_gaussian(forecast, mu, sig)
    # TODO: Add tests for this section.
    else:
        if 'cdf_or_dist' in metric_kwargs:
            cdf_or_dist = metric_kwargs['cdf_or_dist']
        else:

def _check_only_climpred_dims(pe):
    """Warns if dimensions other than `CLIMPRED_DIMS` are in `PredictionEnsemble`."""
    additional_dims = set(pe.get_initialized().dims) - set(CLIMPRED_DIMS)
    if len(additional_dims) != 0:
        raise DimensionError(
            f'{type(pe.__name__)}.plot() does not allow dimensions other '
            f'than {CLIMPRED_DIMS}, found {additional_dims}. '

def _broadcast_non_CLIMPRED_DIMS_from_forecast_to_verif(forecast, verif):
    """Broadcast missing non CLIMPRED_DIMS from forecast to verif."""
    for d in forecast.dims:
        if d not in CLIMPRED_DIMS and d in forecast.dims and d not in verif.dims:
            verif = verif.expand_dims(d)
            verif = verif.isel({d: [0] * forecast[d].size})
            verif[d] = forecast[d]
    return forecast, verif