def METHOD_NAME():
    """Check that normalized entropy is well well-behaved and in [0, 1]."""
    # test tiny numbers
    for dtype in [np.float16, np.float32, np.float64]:
        info = np.finfo(dtype)
        # some NumPy versions have bugs, therefore we provide a fallback
        # (fallback is the value of the smalles datatype float16)
        smallest_normal = getattr(info, "smallest_normal", 6.104e-05)
        smallest_subnormal = getattr(info, "smallest_subnormal", 6e-08)
        for val in [info.eps, smallest_normal, smallest_subnormal, 0]:
            entropy = get_normalized_entropy(np.array([[1.0, val]], dtype=dtype))
            assert 0.0 <= entropy <= 1.0
    # test multiple _assert_valid_inputs
    entropy = get_normalized_entropy(np.array([[0.0, 1.0], [0.5, 0.5]]))
    assert all((0.0 <= entropy) & (entropy <= 1.0))
    # raise errors for wrong probabilities.
    with pytest.raises(ValueError):
        get_normalized_entropy(np.array([[-1.0, 0.5]]))  # negative
        get_normalized_entropy(np.array([[2.0, 0.5]]))  # larger 1