code: def load_boston(return_X_y=False):
      """Load and return the boston house-prices dataset (regression).
  
      ==============     ==============
      Samples total                 506
      Dimensionality                 13
      Features           real, positive
      Targets             real 5. - 50.
      ==============     ==============
  
      Parameters
      ----------
      return_X_y : boolean, default=False.
          If True, returns ``(data, target)`` instead of a Bunch object.
          See below for more information about the `data` and `target` object.
  
          .. versionadded:: 0.18
  
      Returns
      -------
      data : Bunch
          Dictionary-like object, the interesting attributes are:
          'data', the data to learn, 'target', the regression targets,
          and 'DESCR', the full description of the dataset.
  
      (data, target) : tuple if ``return_X_y`` is True
  
          .. versionadded:: 0.18
  
      Examples
      --------
      >>> from sklearn.datasets import load_boston
      >>> boston = load_boston()
      >>> print(boston.data.shape)
      (506, 13)
      """
      module_path = datasets_dir()
  
      fdescr_name = join(
          module_path, "descr", "boston_house_prices.rst"
      )
      with open(fdescr_name) as f:
          descr_text = f.read()
  
      data_file_name = join(
          module_path, "data", "boston_house_prices.csv"
      )
      with open(data_file_name) as f:
          data_file = csv.reader(f)
          temp = next(data_file)
          n_samples = int(temp[0])
          n_features = int(temp[1])
          data = np.empty((n_samples, n_features))
          target = np.empty((n_samples,))
          temp = next(data_file)  # names of features
          feature_names = np.array(temp)
  
          for i, d in enumerate(data_file):
              data[i] = np.asarray(d[:-1], dtype=np.float64)
              target[i] = np.asarray(d[-1], dtype=np.float64)
  
      if return_X_y:
          return data, target
  
      return Bunch(
          data=data,
          target=target,
          # last column is target value
          feature_names=feature_names[:-1],
          DESCR=descr_text,
      )
  
imports: import os
  import csv
  import sys
  import shutil
  from os import environ
  from os.path import dirname
  from os.path import join
  from os.path import exists
  from os.path import expanduser
  from os.path import isdir
  from os.path import splitext
  from os import listdir
  from os import makedirs
  import numpy
  from ..utils import check_random_state
label_desc: Use image node_id 10 for calling a global function with example usage: load_boston(return_X_y) and returns: Bunch, data,  target