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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 load_wikiqa():
 """A Challenge Dataset for Open-Domain Question Answering. WikiQA dataset is a publicly available set of question and sentence (QS) pairs, collected and annotated for research on open-domain question answering. source: "Microsoft" sourceURI: "https://www.microsoft.com/en-us/research/publication/wikiqa-a-challenge-dataset-for-open-domain-question-answering/#" """
  # noqa

    dataset_path = _load('wikiqa')

    data = _load_csv(dataset_path, 'data', set_index=True)
    questions = _load_csv(dataset_path, 'questions', set_index=True)
    sentences = _load_csv(dataset_path, 'sentences', set_index=True)
    vocabulary = _load_csv(dataset_path, 'vocabulary', set_index=True)

    entities = {
        'data': (data, 'd3mIndex', None),
        'questions': (questions, 'qIndex', None),
        'sentences': (sentences, 'sIndex', None),
        'vocabulary': (vocabulary, 'index', None)
    }
    relationships = [
        ('questions', 'qIndex', 'data', 'qIndex'),
        ('sentences', 'sIndex', 'data', 'sIndex')
    ]

    target = data.pop('isAnswer').values

    return Dataset(load_wikiqa.__doc__, data, target, accuracy_score, startify=True,
                   entities=entities, relationships=relationships)