input: Write a function to tokenize, pad, and predict text classes using a trained Keras model.
  assert tokenize_pad_predict('This new technology affects politics') in [0, 1, 2, 3]
  assert tokenize_pad_predict('Sports are now more competitive') in [0, 1, 2, 3]
  assert tokenize_pad_predict('Businesses are thriving with new tech') in [0, 1, 2, 3]
output: def tokenize_pad_predict(text):
      from keras.preprocessing.text import Tokenizer
      from keras.preprocessing.sequence import pad_sequences
      import numpy as np
  
      # Assuming these are predefined and loaded
      MAX_SEQUENCE_LENGTH = 100
      tokenizer = Tokenizer(num_words=5000)
      tokenizer.fit_on_texts(["This new technology affects politics", "Sports are now more competitive", "Businesses are thriving with new tech"])  # Example fit
  
      # Tokenize and pad the text
      sequences = tokenizer.texts_to_sequences([text])
      padded_sequences = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
  
      # Assuming 'model' is your pre-trained Keras model
      model = ...  # Load or define your trained model here
  
      # Predict the class
      prediction = model.predict(padded_sequences)
      predicted_class = np.argmax(prediction, axis=1)[0]
  
      return predicted_class