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(input_utterance: str) -> str:
      """Creates a model for generating natural language responses to input utterances.
      The model uses an embedding layer followed by a GRU layer and a dense layer with a softmax activation.
      The model is trained to predict the correct response for a given input utterance.
      
      >>> generate_response("What time is it?")
      """
      # 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
      query = "What time is it?"
      prediction = model.predict(query)
  
      return prediction
  
output:     
      # Generate the response to the input utterance
      query = input_utterance
      prediction = model.predict(query)
  
      return prediction
  