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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 explain_prediction_linear_classifier(clf, doc, vec=None, top=None, top_targets=None, target_names=None, targets=None, feature_names=None, feature_re=None, feature_filter=None, vectorized=False, ):
 """ Explain prediction of a linear classifier. See :func:`eli5.explain_prediction` for description of ``top``, ``top_targets``, ``target_names``, ``targets``, ``feature_names``, ``feature_re`` and ``feature_filter`` parameters. ``vec`` is a vectorizer instance used to transform raw features to the input of the classifier ``clf`` (e.g. a fitted CountVectorizer instance); you can pass it instead of ``feature_names``. ``vectorized`` is a flag which tells eli5 if ``doc`` should be passed through ``vec`` or not. By default it is False, meaning that if ``vec`` is not None, ``vec.transform([doc])`` is passed to the classifier. Set it to True if you're passing ``vec``, but ``doc`` is already vectorized. """

    vec, feature_names = handle_vec(clf, doc, vec, vectorized, feature_names)
    X = get_X(doc, vec=vec, vectorized=vectorized, to_dense=True)

    proba = predict_proba(clf, X)
    score, = clf.decision_function(X)

    if has_intercept(clf):
        X = add_intercept(X)
    x = get_X0(X)

    feature_names, flt_indices = feature_names.handle_filter(
        feature_filter, feature_re, x)

    res = Explanation(
        estimator=repr(clf),
        method='linear model',
        targets=[],
    )
    assert res.targets is not None

    _weights = _linear_weights(clf, x, top, feature_names, flt_indices)
    classes = getattr(clf, "classes_", ["-1", "1"])  # OneClassSVM support
    display_names = get_target_display_names(classes, target_names,
                                             targets, top_targets, score)

    if is_multiclass_classifier(clf):
        for label_id, label in display_names:
            target_expl = TargetExplanation(
                target=label,
                feature_weights=_weights(label_id),
                score=score[label_id],
                proba=proba[label_id] if proba is not None else None,
            )
            add_weighted_spans(doc, vec, vectorized, target_expl)
            res.targets.append(target_expl)
    else:
        if len(display_names) == 1:  # target is passed explicitly
            label_id, target = display_names[0]
        else:
            label_id = 1 if score >= 0 else 0
            target = display_names[label_id][1]
        scale = -1 if label_id == 0 else 1

        target_expl = TargetExplanation(
            target=target,
            feature_weights=_weights(0, scale=scale),
            score=score,
            proba=proba[label_id] if proba is not None else None,
        )
        add_weighted_spans(doc, vec, vectorized, target_expl)
        res.targets.append(target_expl)

    return res