How to use the climpred.exceptions.DimensionError function in climpred

'sub': '-',
            'mul': '*',
            'div': '/',
        }
        error_str = f'Cannot use {type(self)} {OPERATOR_STR[operator]} {type(other)}'

        # catch undefined types for other
        if not isinstance(other, tuple(ALLOWED_TYPES_FOR_MATH_OPERATORS)):
            raise TypeError(
                f'{error_str} because type {type(other)} not supported. '
                f'Please choose from {ALLOWED_TYPES_FOR_MATH_OPERATORS}.'
            )
        # catch other dimensions in other
        if isinstance(other, tuple([xr.Dataset, xr.DataArray])):
            if not set(other.dims).issubset(self._datasets['initialized'].dims):
                raise DimensionError(f'{error_str} containing new dimensions.')
        # catch xr.Dataset with different data_vars
        if isinstance(other, xr.Dataset):
            if list(other.data_vars) != list(self._datasets['initialized'].data_vars):
                raise VariableError(
                    f'{error_str} with new `data_vars`. Please use {type(self)} '
                    f'{operator} {type(other)} only with same `data_vars`. Found '
                    f'initialized.data_vars = '
                    f' {list(self._datasets["initialized"].data_vars)} vs. '
                    f'other.data_vars = { list(other.data_vars)}.'
                )

        operator = eval(operator)

        # Create temporary copy to modify to avoid inplace operation.
        datasets = self._datasets.copy()
        for dataset in datasets:

"""Checks that the verification data dimensions match appropriate initialized
    dimensions.

    If uninitialized, ignore ``member``. Otherwise, ignore ``lead`` and ``member``.
    """
    # Since verification data won't have the ``init``` dimension, temporarily rename to
    # time.
    init = init.rename({'init': 'time'})
    init_dims = list(init.dims)
    if 'lead' in init_dims:
        init_dims.remove('lead')
    if ('member' in init_dims) and not uninitialized:
        init_dims.remove('member')
    if not (set(verif.dims) == set(init_dims)):
        unmatch_dims = set(verif.dims) ^ set(init_dims)
        raise DimensionError(
            'Dimensions must match initialized prediction ensemble '
            f'dimensions; these dimensions do not match: {unmatch_dims}.'
        )
    return True

Returns:
        ds (xarray object): xr.Dataset/xr.DataArray with less members.

    Raises:
        DimensionError: if list items are not all in ds.member

    """
    if removed_member is None:
        removed_member = [0]
    if all(m in ds.member.values for m in removed_member):
        member_list = list(ds.member.values)
        for ens in removed_member:
            member_list.remove(ens)
    else:
        raise DimensionError('select available members only')
    return ds.sel(member=member_list)

Reference:
                  * https://stackoverflow.com/questions/1528237/
                    how-to-handle-exceptions-in-a-list-comprehensions
                """
                try:
                    return getattr(v, name)(*args, **kwargs)
                # ValueError : Cases such as .sum(dim='time'). This doesn't apply
                #              it to the given dataset if the dimension doesn't exist.
                # KeyError : Cases where a function calls the index of a Dataset. Such
                #            as ds[dim] and the dim doesn't exist as a key.
                # DimensionError: This accounts for our custom error when applying
                # some stats functions.
                # NOTE: Remove the esmtools version once you remove those errors from
                #       esmtools.
                except (ValueError, KeyError, DimensionError):
                    return v