def METHOD_NAME():
    """Check the equivalence between between sparse and dense DictVectorizer.
    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/19978
    """
    movie_entry_fit = [
        {"category": ["thriller", "drama"], "year": 2003},
        {"category": ["animation", "family"], "year": 2011},
        {"year": 1974},
    ]
    movie_entry_transform = [{"category": ["thriller"], "unseen_feature": "3"}]
    dense_vectorizer = DictVectorizer(sparse=False)
    sparse_vectorizer = DictVectorizer(sparse=True)
    dense_vector_fit = dense_vectorizer.fit_transform(movie_entry_fit)
    sparse_vector_fit = sparse_vectorizer.fit_transform(movie_entry_fit)
    assert not sp.issparse(dense_vector_fit)
    assert sp.issparse(sparse_vector_fit)
    assert_allclose(dense_vector_fit, sparse_vector_fit.toarray())
    dense_vector_transform = dense_vectorizer.transform(movie_entry_transform)
    sparse_vector_transform = sparse_vectorizer.transform(movie_entry_transform)
    assert not sp.issparse(dense_vector_transform)
    assert sp.issparse(sparse_vector_transform)
    assert_allclose(dense_vector_transform, sparse_vector_transform.toarray())
    dense_inverse_transform = dense_vectorizer.inverse_transform(dense_vector_transform)
    sparse_inverse_transform = sparse_vectorizer.inverse_transform(
        sparse_vector_transform
    )
    expected_inverse = [{"category=thriller": 1.0}]
    assert dense_inverse_transform == expected_inverse
    assert sparse_inverse_transform == expected_inverse