def METHOD_NAME(configuration_name='VOC'):
    configuration = get_prior_box_configuration(configuration_name)
    image_size = configuration['image_size']
    feature_map_sizes = configuration['feature_map_sizes']
    min_sizes = configuration['min_sizes']
    max_sizes = configuration['max_sizes']
    steps = configuration['steps']
    model_aspect_ratios = configuration['aspect_ratios']
    mean = []
    for feature_map_arg, feature_map_size in enumerate(feature_map_sizes):
        step = steps[feature_map_arg]
        min_size = min_sizes[feature_map_arg]
        max_size = max_sizes[feature_map_arg]
        aspect_ratios = model_aspect_ratios[feature_map_arg]
        for y, x in product(range(feature_map_size), repeat=2):
            f_k = image_size / step
            center_x = (x + 0.5) / f_k
            center_y = (y + 0.5) / f_k
            s_k = min_size / image_size
            mean = mean + [center_x, center_y, s_k, s_k]
            s_k_prime = np.sqrt(s_k * (max_size / image_size))
            mean = mean + [center_x, center_y, s_k_prime, s_k_prime]
            for aspect_ratio in aspect_ratios:
                mean = mean + [center_x, center_y, s_k * np.sqrt(aspect_ratio),
                               s_k / np.sqrt(aspect_ratio)]
                mean = mean + [center_x, center_y, s_k / np.sqrt(aspect_ratio),
                               s_k * np.sqrt(aspect_ratio)]
    output = np.asarray(mean).reshape((-1, 4))
    # output = np.clip(output, 0, 1)
    return output