input: Write a function to create a neural network model for generating natural language responses. The function should take a sample utterance and return the prediction generated by the model. The model should be built using TensorFlow's Keras API and trained on input and output data.
  
  def generate_response(utterance):
      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
      prediction = model.predict(utterance)
  
      return prediction
  
  assert generate_response("What time is it?") is not None
  assert generate_response("What is the weather today?") is not None
  assert generate_response("Who is the president?") is not None
  assert generate_response("How are you?") is not None
  assert generate_response("What is your name?") is not None
output: def generate_response(utterance):
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import 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
      inputs = np.random.randint(0, 1000, size=(1000, 20))
      outputs = np.random.randint(0, 1000, size=(1000, 1000))
      model.fit([inputs], [outputs], epochs=10, batch_size=32)
  
      # Generate the response to the sample utterance
      prediction = model.predict(np.array([utterance]))
  
      return prediction