input: 
  
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import plot_model
  
  def generate_response(sample_utterance: str) -> str:
      """ Create a system in Python for generating natural language responses based on sample utterances.
      >>> generate_response("What time is it?")
      'Current time is [time]'
      >>> generate_response("How are you?")
      'I am doing well, thank you!'
      """
      # Create inputs
      inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
  
      # Create outputs
      y1 = Dense(1000, activation='softmax')(x1)
  
      # Create model
      model = Model(inputs=[inputs1], outputs=[y1])
  
      # Compile the model
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      # Train the model
      model.fit([inputs], [outputs],  epochs=10, batch_size=32)
  
      # Generate the response to the sample utterance
      prediction = model.predict(sample_utterance)
  
      return prediction
  
output:     
      # Preprocess the sample utterance
      sample_utterance = sample_utterance.lower()
      sample_utterance = sample_utterance.split()
  
      # Convert the utterance to a numerical input
      input_sequence = [word_to_index[word] for word in sample_utterance if word in word_to_index]
      input_sequence = pad_sequences([input_sequence], maxlen=20)
  
      # Generate the response
      response_index = np.argmax(model.predict(input_sequence), axis=-1)[0]
      response = index_to_word[response_index]
  
      return response
  