input: def predict_house_price(features: np.array) -> float:
      """ Implement a neural network to predict the price of a house given a set of features.
      The neural network is designed using Keras, with 2 hidden layers and an output layer.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
      model.fit(features, batch_size=3, epochs=200)
      """
output:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, input_dim=features.shape[1], activation='relu'))
      model.add(Dense(5, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
      model.fit(features, np.zeros((features.shape[0], 1)), batch_size=3, epochs=200, verbose=0)
  
      return model.predict(features)[0][0]