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 trained model. If the input features are empty, return None.
  assert house_price_predictor([0.8, 0.4, 0.7]) != None
  assert house_price_predictor([]) == None
output: def house_price_predictor(features):
      if not features:
          return None
      
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, input_dim=len(features), activation='relu'))
      model.add(Dense(1, activation='linear'))
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      # Dummy training data
      X_train = np.array([features])
      y_train = np.array([0.5])  # Dummy target value
      
      model.fit(X_train, y_train, epochs=1, verbose=0)
      
      return model