input: def generate_natural_language_response(utterance: str) -> str:
      """
      This function generates a natural language response based on the given utterance.
      It uses a pre-trained neural network model that has been trained on various utterances
      and corresponding responses.
      
      >>> generate_natural_language_response("What time is it?")
      "It's 10:00 AM."
      >>> generate_natural_language_response("How are you?")
      "I'm good, thanks for asking!"
      """
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import Model
      from tensorflow.keras.utils import plot_model
  
      # 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 model weights
      model.load_weights('pretrained_model.h5')
  
      # Preprocess the utterance
      import numpy as np
      from tensorflow.keras.preprocessing.text import Tokenizer
      from tensorflow.keras.preprocessing.sequence import pad_sequences
  
      tokenizer = Tokenizer(num_words=1000)
      tokenizer.fit_on_texts([utterance])
      sequence = tokenizer.texts_to_sequences([utterance])
      padded_sequence = pad_sequences(sequence, maxlen=20)
  
      # Generate response
      response = model.predict(padded_sequence)
      response_text = tokenizer.sequences_to_texts(np.argmax(response, axis=1))[0]
  
      return response_text