input: Write a python function to tokenize and pad sequences from a given list of phrases.
  assert tokenize_and_pad(['This is a test', 'Another test'])==([[2, 3, 1, 4], [5, 1]], 5, 6)
  assert tokenize_and_pad(['Hello world', 'How are you'])==([[2, 1], [3, 4, 5]], 4, 5)
  assert tokenize_and_pad(['Tokenize this', 'Pad this sequence'])==([[2, 1], [3, 1, 4]], 4, 5)
output: def tokenize_and_pad(phrases):
      from keras.preprocessing.text import Tokenizer
      from keras.preprocessing.sequence import pad_sequences
  
      # Initialize the tokenizer
      tokenizer = Tokenizer()
      # Fit the tokenizer on the phrases
      tokenizer.fit_on_texts(phrases)
      
      # Convert phrases to sequences
      sequences = tokenizer.texts_to_sequences(phrases)
      
      # Find the length of the longest sequence
      max_len = max(len(seq) for seq in sequences)
      
      # Pad the sequences
      padded_sequences = pad_sequences(sequences, padding='post')
      
      # Number of unique tokens
      num_tokens = len(tokenizer.word_index) + 1  # +1 for the zero-padding index
      
      return padded_sequences.tolist(), num_tokens, max_len