input: 
  def predict_house_price(features: np.array) -> float:
      """Predict the price of a house given a set of features using a pre-trained neural network.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      250000.0
      """
      model = Sequential()
      model.add(Dense(64, input_dim=3, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
  
      # Assuming the model has already been trained and weights are loaded
      return model.predict(features)
  
output:     model.load_weights('house_price_model_weights.h5')
      prediction = model.predict(features)
      return float(prediction[0][0])