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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def face_distance(face_encodings, face_to_compare):
 """ Given a list of face encodings, compare them to a known face encoding and get a euclidean distance for each comparison face. The distance tells you how similar the faces are. :param faces: List of face encodings to compare :param face_to_compare: A face encoding to compare against :return: A numpy ndarray with the distance for each face in the same order as the 'faces' array """

    if len(face_encodings) == 0:
        return np.empty((0))

    return np.linalg.norm(face_encodings - face_to_compare, axis=1)