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(model, utterance):
      """ Generates a natural language response based on the given utterance using a pre-trained model.
      >>> model = Model(inputs=[inputs1], outputs=[y1])
      >>> model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      >>> model.fit([inputs], [outputs], epochs=10, batch_size=32)
      >>> generate_response(model, "What time is it?")
      [[<prediction_output>]]
      """
      prediction = model.predict(utterance)
      return prediction
  
output:     input_layer = Input(shape=(utterance.shape[1],))
      embedding_layer = Embedding(input_dim=10000, output_dim=128)(input_layer)
      gru_layer = GRU(128)(embedding_layer)
      output_layer = Dense(1, activation='sigmoid')(gru_layer)
      model = Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      model.fit(utterance, epochs=10, batch_size=32)
      prediction = model.predict(utterance)
      return prediction