input: Write a python function to preprocess text data using the Keras Tokenizer.
  assert len(preprocess_text(["The quick brown fox jumps over the lazy dog"], num_words=1000)[0]) == 1000
  assert preprocess_text(["The quick brown fox"], num_words=10)[0][0] == 1
  assert preprocess_text(["A quick brown fox"], num_words=5)[0][1] == 1
output: def preprocess_text(texts, num_words):
      from keras.preprocessing.text import Tokenizer
      from keras.preprocessing.sequence import pad_sequences
  
      tokenizer = Tokenizer(num_words=num_words)
      tokenizer.fit_on_texts(texts)
      sequences = tokenizer.texts_to_sequences(texts)
      padded_sequences = pad_sequences(sequences, maxlen=num_words, padding='post')
  
      return padded_sequences