input: Write a python function to create a Word2Vec model from a list of sentences and return the vocabulary.
  assert set(create_word2vec_model(["this is a sentence", "this is also a sentence", "this sentence is not like the others"])) == {"this", "is", "a", "sentence", "also", "not", "like", "the", "others"}
  assert set(create_word2vec_model(["cat and dog", "dog and mouse", "mouse and cat"])) == {"cat", "and", "dog", "mouse"}
output: def create_word2vec_model(sentences):
      from gensim.models import Word2Vec
      from nltk.tokenize import word_tokenize
  
      # Tokenize the sentences
      tokenized_sentences = [word_tokenize(sentence) for sentence in sentences]
  
      # Create and train the Word2Vec model
      model = Word2Vec(sentences=tokenized_sentences, vector_size=100, window=5, min_count=1, workers=4)
  
      # Return the vocabulary
      return list(model.wv.index_to_key)