input: 
  def predict_house_price(features: np.ndarray) -> float:
      """
      Predict the price of a house given a set of features using a neural network model.
  
      Example:
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      178000.0
      """
      model = Sequential()
      model.add(Dense(64, input_dim=features.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
  
      # Dummy training for illustration purposes
      dummy_features = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
      dummy_prices = np.array([150000, 200000])
  
      model.fit(dummy_features, dummy_prices, batch_size=2, epochs=5)
  
      predicted_price = model.predict(features)
      return float(predicted_price[0][0])
  
output:     from keras.models import Sequential
      from keras.layers import Dense
      import numpy as np