input: Write a python function to tokenize sentences using nltk and create a vocabulary. 
  assert tokenize_and_create_vocab([['Hi', 'How are you?'], ['I am good', 'That is awesome'], ['Thank you']]) == (['Hi', 'How are you?', 'I am good', 'That is awesome', 'Thank you'], {'Hi': 0, 'How are you?': 1, 'I am good': 2, 'That is awesome': 3, 'Thank you': 4})
  assert tokenize_and_create_vocab([['Hello world'], ['Goodbye world']]) == (['Hello world', 'Goodbye world'], {'Hello world': 0, 'Goodbye world': 1})
  assert tokenize_and_create_vocab([['Yes'], ['No'], ['Maybe']]) == (['Yes', 'No', 'Maybe'], {'Yes': 0, 'No': 1, 'Maybe': 2})
output: def tokenize_and_create_vocab(sentences_list):
      from nltk.tokenize import sent_tokenize
      
      # Flatten the list of lists into a single list of sentences
      sentences = [sentence for sublist in sentences_list for sentence in sublist]
      
      # Create a vocabulary dictionary
      vocab = {sentence: idx for idx, sentence in enumerate(sentences)}
      
      return sentences, vocab