input: Write a function to design and implement a neural network in Python to predict the price of a house given a set of features. The neural network should be implemented using Keras, with a Sequential model containing Dense layers. The function should return the compiled model.
  assert design_neural_network([0.8, 0.4, 0.7]).predict(np.array([[0.8, 0.4, 0.7]])) is not None
  assert design_neural_network([0.8, 0.4, 0.7]).compile(loss='mse', optimizer='adam', metrics=['mse']) is not None
  assert design_neural_network([0.5, 0.3, 0.6]).fit(np.array([[0.5, 0.3, 0.6]]), batch_size=3, epochs=200) is not None
  assert design_neural_network([0.9, 0.1, 0.4]).get_weights() is not None
  assert design_neural_network([0.2, 0.7, 0.5]).evaluate(np.array([[0.2, 0.7, 0.5]]), np.array([0.0]))[0] is not None
output: 
  def design_neural_network(features):
      from keras.models import Sequential
      from keras.layers import Dense
      model = Sequential()
      model.add(Dense(64, input_dim=len(features), activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
      return model
  