input: Write a function to train a Linear Regression model in Python that can predict housing prices given the living area and the number of rooms. The function should take an input array of data, where each element is a list containing the living area and the number of rooms, and a corresponding output array containing the housing prices. The function should return the trained model.
  
  from sklearn import linear_model
  import numpy as np
  
  def train_housing_price_model(data, prices):
      X = np.array(data)
      y = np.array(prices)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model
  
  assert type(train_housing_price_model([[2000, 3], [1600, 2.5]], [75000, 43000]))==linear_model.LinearRegression
  assert type(train_housing_price_model([[1400, 2]], [32000]))==linear_model.LinearRegression
  assert type(train_housing_price_model([], []))==linear_model.LinearRegression
output: def train_housing_price_model(data, prices):
      X = np.array(data)
      y = np.array(prices)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model