def noisy(
    image: NDArray,
    ratio: float,
    seed: int = None,
) -> NDArray:
    """Generates a noisy image by adding random noise to the content_image"""
    if ratio < 0 or 1 < ratio:
        raise InvalidRatioError("Expected a ratio value x such that 0 <= x <= 1")

    noise_image = np.random.uniform(-20, 20, image.shape).astype("float32")

    # Set the input_image to be a weighted average of the content_image and a noise_image
    return noise_image * ratio + image * (1 - ratio)

def subtract(image: NDArray, array: NDArray) -> NDArray:
    """Normalize the input image.

    Args:
        image (NDArray): [description]

    Raises:
        ShapeMissmatchError: in case of ValueError due to numpy broadcasting failing

    Returns:
        NDArray: [description]
    """
    try:
        return image - array
    except ValueError as numpy_broadcast_error:
        raise ShapeMissmatchError(
            "Expected arrays with matching shapes."
        ) from numpy_broadcast_error