def METHOD_NAME():
    # Test score_samples (pseudo-likelihood) method.
    # Assert that pseudo-likelihood is computed without clipping.
    # See Fabian's blog, http://bit.ly/1iYefRk
    rng = np.random.RandomState(42)
    X = np.vstack([np.zeros(1000), np.ones(1000)])
    rbm1 = BernoulliRBM(n_components=10, batch_size=2, n_iter=10, random_state=rng)
    rbm1.fit(X)
    assert (rbm1.score_samples(X) < -300).all()
    # Sparse vs. dense should not affect the output. Also test sparse input
    # validation.
    rbm1.random_state = 42
    d_score = rbm1.score_samples(X)
    rbm1.random_state = 42
    s_score = rbm1.score_samples(lil_matrix(X))
    assert_almost_equal(d_score, s_score)
    # Test numerical stability (#2785): would previously generate infinities
    # and crash with an exception.
    with np.errstate(under="ignore"):
        rbm1.score_samples([np.arange(1000) * 100])