input: Write a python function to parse a user's input into word vectors and average them.
  assert np.array_equal(parse_user_input("hello"), np.array([1, 2, 3, 4]))
  assert np.array_equal(parse_user_input("world"), np.array([2, 3, 4, 5]))
  assert np.array_equal(parse_user_input("hello world"), np.array([1.5, 2.5, 3.5, 4.5]))
  assert np.array_equal(parse_user_input("unknown"), np.zeros((4,)))
output: import numpy as np
  
  # Mock word vectors for example purposes
  word_vectors = {
      "hello": np.array([1, 2, 3, 4]),
      "world": np.array([2, 3, 4, 5])
  }
  
  def parse_user_input(user_input):
      words = user_input.split()
      vectors = []
      for word in words:
          if word in word_vectors:
              vectors.append(word_vectors[word])
          else:
              vectors.append(np.zeros((4,)))
      if vectors:
          return np.mean(vectors, axis=0)
      else:
          return np.zeros((4,))