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. Return the trained model. If the given feature set is empty, return None.
  import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense
  
  features1 = np.array([[0.8, 0.4, 0.7]])
  output1 = Sequential()
  output1.add(Dense(64, input_dim=3, activation='relu'))
  output1.add(Dense(32, activation='relu'))
  output1.add(Dense(1, activation='linear'))
  output1.compile(loss='mse', optimizer='adam', metrics=['mse'])
  output1.fit(features1, batch_size=3, epochs=200)
  
  assert type(predict_house_price(features1)) == Sequential
  assert predict_house_price([]) == None
output: def predict_house_price(features):
      if not features:
          return None
      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'))
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
      model.fit(features, batch_size=3, epochs=200)
      return model