input: def predict_house_price(features: np.array) -> None:
      """Design and implement a neural network in Python to predict the price of a house given a set of features.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      """
output:     
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Define the model
      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'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Predict the house price
      prediction = model.predict(features)
      print(prediction)
  