input: Write a function to design and implement a neural network in Python using Keras to predict the price of a house given a set of features. The function should return the compiled model ready for training. Provide the function with an input feature array and compile the model with mean squared error (MSE) as the loss function and 'adam' as the optimizer.
  assert design_house_price_predictor(np.array([[0.8, 0.4, 0.7]])) is not None
  model = design_house_price_predictor(np.array([[0.8, 0.4, 0.7]]))
  assert model.input_shape == (None, 3)
  assert model.output_shape == (None, 1)
output: def design_house_price_predictor(input_features):
      from keras.models import Sequential
      from keras.layers import Dense
      
      model = Sequential()
      model.add(Dense(10, input_shape=(input_features.shape[1],), activation='relu'))
      model.add(Dense(1, activation='linear'))
      model.compile(loss='mean_squared_error', optimizer='adam')
      return model