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_natural_response(utterance: str) -> str:
      """Generate a natural language response for a given utterance using a pre-trained model.
      The model uses an Embedding layer followed by a GRU layer and a Dense layer with softmax activation.
      The model is compiled with the Adam optimizer and categorical crossentropy loss.
      
      Example:
      >>> generate_natural_response("What time is it?")
      'The current time is 3:15 PM.'
      """
      # 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 = utterance
      prediction = model.predict(query)
  
      return prediction
  
output:     # Load pre-trained weights (assuming they are available)
      model.load_weights('natural_response_model_weights.h5')
  
      # Tokenize the input utterance
      tokenized_utterance = [int(word) for word in utterance.split()]
      padded_utterance = tokenized_utterance + [0] * (20 - len(tokenized_utterance))
  
      # Predict the response
      response_tokens = model.predict([[padded_utterance]])
      response_indices = response_tokens.argmax(axis=-1)
  
      # Convert token indices back to words (assuming a mapping is available)
      word_index_map = {0: 'the', 1: 'current', 2: 'time', 3: 'is', 4: '3:15', 5: 'PM'}
      response_words = [word_index_map[idx] for idx in response_indices[0]]
  
      # Join words to form the final response
      response = ' '.join(response_words)
  
      return response