input: 
  def predict_house_price(features: List[float]) -> np.ndarray:
      """
      Design and implement a neural network in Python to predict the price of a house given a set of features.
      The neural network is a Sequential model with layers:
      - First layer: Dense layer with 64 units, input dimension of 3, and activation function 'relu'.
      - Second layer: Dense layer with 32 units and activation function 'relu'.
      - Third layer: Dense layer with 1 unit and activation function 'linear'.
      The model is compiled with mean squared error loss and Adam optimizer, using mean squared error as a metric.
      
      >>> predict_house_price([0.8, 0.4, 0.7])
      array(...)
      """
      features_array = np.array([features])
  
      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'])
  
      model.fit(features_array, batch_size=3, epochs=200)
  
      return model.predict(features_array)
  
  features = [0.8, 0.4, 0.7]
  print(predict_house_price(features))
  
output:     
      features_array = np.array([features])
  
      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'])
  
      model.fit(features_array, batch_size=3, epochs=200)
  
      return model.predict(features_array)
  