code: def boston_housing_data():
      """Boston Housing dataset.
  
      Source : https://archive.ics.uci.edu/ml/datasets/Housing
      Number of samples : 506
  
      Continuous target variable : MEDV
      MEDV = Median value of owner-occupied homes in $1000's
  
      Dataset Attributes:
  
          - 1) CRIM      per capita crime rate by town
          - 2) ZN        proportion of residential land zoned for lots over
                   25,000 sq.ft.
          - 3) INDUS     proportion of non-retail business acres per town
          - 4) CHAS      Charles River dummy variable (= 1 if tract bounds
                   river; 0 otherwise)
          - 5) NOX       nitric oxides concentration (parts per 10 million)
          - 6) RM        average number of rooms per dwelling
          - 7) AGE       proportion of owner-occupied units built prior to 1940
          - 8) DIS       weighted distances to five Boston employment centres
          - 9) RAD       index of accessibility to radial highways
          - 10) TAX      full-value property-tax rate per $10,000
          - 11) PTRATIO  pupil-teacher ratio by town
          - 12) B        1000(Bk - 0.63)^2 where Bk is the prop. of b. by town
          - 13) LSTAT    % lower status of the population
  
      Returns
      --------
      X, y : [n_samples, n_features], [n_class_labels]
          X is the feature matrix with 506 housing samples as rows
          and 13 feature columns.
          y is a 1-dimensional array of the continuous target variable MEDV
  
      Examples
      -----------
      For usage examples, please see
      https://rasbt.github.io/mlxtend/user_guide/data/boston_housing_data/
  
      """
      tmp = np.genfromtxt(fname=DATA_PATH, delimiter=",", dtype=float)
      X, y = tmp[:, :-1], tmp[:, -1]
      return X, y
  
label_desc: Use image node_id 1 for calling a global function with example usage: boston_housing_data() and returns: X,  y