input: from typing import List, Tuple
  
  def predict_housing_price(data: List[Tuple[int, float]], model_output: Tuple[List[int], List[int]], new_data: Tuple[int, float]) -> float:
      """ Predicts the housing price for a given living area and number of rooms using a trained Linear Regression model.
      The model is trained on sample data where each data point is a tuple of living area in square feet and number of rooms.
      The output is the predicted price for a new data point of the same format.
      
      >>> data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
      >>> model_output_X = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
      >>> model_output_y = [75000, 43000, 59000, 32000, 85000]
      >>> new_data_point = [2200, 3]
      >>> predict_housing_price(data, (model_output_X, model_output_y), new_data_point)
      79000
      """
      
      import numpy as np
      from sklearn import linear_model
      
      X = np.array(model_output[0])
      y = np.array(model_output[1])
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([new_data])
      return prediction[0]
output:     import numpy as np
      from sklearn import linear_model
      
      X = np.array(model_output[0])
      y = np.array(model_output[1])
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([new_data])
      return prediction[0]