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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def filtered(self, feature_filter, x=None):
 # type: (Callable, Any) -> Tuple[FeatureNames, List[int]] """ Return feature names filtered by a regular expression ``feature_re``, and indices of filtered elements. """

        indices = []
        filtered_feature_names = []
        indexed_names = None  # type: Optional[Iterable[Tuple[int, Any]]]
        if isinstance(self.feature_names, (np.ndarray, list)):
            indexed_names = enumerate(self.feature_names)
        elif isinstance(self.feature_names, dict):
            indexed_names = six.iteritems(self.feature_names)
        elif self.feature_names is None:
            indexed_names = []
        assert indexed_names is not None

        if x is not None:
            if sp.issparse(x) and len(x.shape) == 2:
                assert x.shape[0] == 1
                flt = lambda nm, i: feature_filter(nm, x[0, i])
            else:
                # FIXME: mypy warns about x[i] because it thinks x can be None
                flt = lambda nm, i: feature_filter(nm, x[i])  # type: ignore
        else:
            flt = lambda nm, i: feature_filter(nm)

        for idx, name in indexed_names:
            if any(flt(nm, idx) for nm in _all_feature_names(name)):
                indices.append(idx)
                filtered_feature_names.append(name)
        if self.has_bias and flt(self.bias_name, self.bias_idx):
            assert self.bias_idx is not None  # for mypy
            bias_name = self.bias_name
            indices.append(self.bias_idx)
        else:
            bias_name = None
        return (
            FeatureNames(
                filtered_feature_names,
                bias_name=bias_name,
                unkn_template=self.unkn_template,
            ),
            indices)