input: Write a function to create a natural language response generator using a neural network model in Python. The function should create and train a model using TensorFlow, and then generate a response to a given utterance. Specifically, the function should include the model's layers, compilation, training, and prediction process.
  
  assert generate_response_model('What time is it?')==prediction
  
output: def generate_response_model(utterance):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, LSTM, Embedding
      from tensorflow.keras.optimizers import Adam
  
      # Sample data for training (replace with actual data)
      input_data = ["What time is it?", "How are you?", "What's up?"]
      output_data = ["It's 3 PM.", "I'm fine, thank you.", "Not much, you?"]
  
      # Tokenize input and output data
      tokenizer = tf.keras.preprocessing.text.Tokenizer()
      tokenizer.fit_on_texts(input_data + output_data)
      input_sequences = tokenizer.texts_to_sequences(input_data)
      output_sequences = tokenizer.texts_to_sequences(output_data)
  
      # Pad sequences to ensure uniform length
      max_len = max(len(seq) for seq in input_sequences + output_sequences)
      input_sequences = tf.keras.preprocessing.sequence.pad_sequences(input_sequences, maxlen=max_len, padding='post')
      output_sequences = tf.keras.preprocessing.sequence.pad_sequences(output_sequences, maxlen=max_len, padding='post')
  
      # Create the model
      model = Sequential([
          Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=64, input_length=max_len),
          LSTM(128, return_sequences=True),
          Dense(len(tokenizer.word_index) + 1, activation='softmax')
      ])
  
      # Compile the model
      model.compile(optimizer=Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model (replace with actual training data)
      model.fit(input_sequences, output_sequences, epochs=10, batch_size=1)
  
      # Generate a response
      input_sequence = tokenizer.texts_to_sequences([utterance])
      input_sequence = tf.keras.preprocessing.sequence.pad_sequences(input_sequence, maxlen=max_len, padding='post')
      prediction = model.predict(input_sequence)
      predicted_sequence = [tf.argmax(word_probs, axis=1).numpy()[0] for word_probs in prediction]
      response = tokenizer.sequences_to_texts([predicted_sequence])[0]
  
      return response