input: Write a function to create a natural language response generation system in Python using TensorFlow and Keras. The function should compile the model, train it, and return the prediction for a given sample utterance. If the input is empty or invalid, the function should return None.
  assert generate_response("What time is it?")==prediction
  assert generate_response("")==None
  assert generate_response(None)==None
output: def generate_response(utterance):
      if not utterance:
          return None
      
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, LSTM, Embedding
      from tensorflow.keras.preprocessing.text import Tokenizer
      from tensorflow.keras.preprocessing.sequence import pad_sequences
  
      # Sample data
      texts = ["What time is it?", "How are you?", "What's the weather like?"]
      responses = ["It's 3 PM.", "I'm fine, thank you.", "It's sunny."]
  
      # Tokenize the data
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(texts)
      sequences = tokenizer.texts_to_sequences(texts)
      max_len = max(len(seq) for seq in sequences)
      padded_sequences = pad_sequences(sequences, maxlen=max_len, padding='post')
  
      # Model creation
      model = Sequential()
      model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=100, input_length=max_len))
      model.add(LSTM(128))
      model.add(Dense(len(responses), activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(padded_sequences, [responses.index(resp) for resp in responses], epochs=10)
  
      # Predict the response
      input_seq = tokenizer.texts_to_sequences([utterance])
      padded_input_seq = pad_sequences(input_seq, maxlen=max_len, padding='post')
      prediction = model.predict(padded_input_seq)
      response_index = tf.argmax(prediction, axis=1).numpy()[0]
      return responses[response_index]