input: Write a function to create a Linear Regression model in Python that predicts housing prices based on the living area and number of rooms. The function should return the trained model. If the input data is empty or invalid, the function should return None.
  
  assert create_linear_regression_model([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]],[75000, 43000, 59000, 32000, 85000]) != None
  assert create_linear_regression_model([],[]) == None
  assert create_linear_regression_model([[2000, 3], [1600, 2.5]], []) == None
output: def create_linear_regression_model(X, y):
      if not X or not y or len(X) != len(y):
          return None
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return model