input: Write a python function to embed a sentence using a pre-trained Word2Vec model.
  assert embed_sentence('word embeddings are very useful', model) == len('word embeddings are very useful'.split())
  assert len(embed_sentence('testing word embedding function', model)) == len('testing word embedding function'.split())
  assert embed_sentence('another test sentence', model) != []
output: def embed_sentence(sentence, model):
      words = sentence.split()
      embeddings = [model[word] for word in words if word in model]
      return embeddings