code: def __getattr__(name):
      if name == "load_boston":
          msg = textwrap.dedent(
              """
              `load_boston` has been removed from scikit-learn since version 1.2.
  
              The Boston housing prices dataset has an ethical problem: as
              investigated in [1], the authors of this dataset engineered a
              non-invertible variable "B" assuming that racial self-segregation had a
              positive impact on house prices [2]. Furthermore the goal of the
              research that led to the creation of this dataset was to study the
              impact of air quality but it did not give adequate demonstration of the
              validity of this assumption.
  
              The scikit-learn maintainers therefore strongly discourage the use of
              this dataset unless the purpose of the code is to study and educate
              about ethical issues in data science and machine learning.
  
              In this special case, you can fetch the dataset from the original
              source::
  
                  import pandas as pd
                  import numpy as np
  
                  data_url = "http://lib.stat.cmu.edu/datasets/boston"
                  raw_df = pd.read_csv(data_url, sep="\\s+", skiprows=22, header=None)
                  data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
                  target = raw_df.values[1::2, 2]
  
              Alternative datasets include the California housing dataset and the
              Ames housing dataset. You can load the datasets as follows::
  
                  from sklearn.datasets import fetch_california_housing
                  housing = fetch_california_housing()
  
              for the California housing dataset and::
  
                  from sklearn.datasets import fetch_openml
                  housing = fetch_openml(name="house_prices", as_frame=True)
  
              for the Ames housing dataset.
  
              [1] M Carlisle.
              "Racist data destruction?"
              <https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>
  
              [2] Harrison Jr, David, and Daniel L. Rubinfeld.
              "Hedonic housing prices and the demand for clean air."
              Journal of environmental economics and management 5.1 (1978): 81-102.
              <https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>
              """
          )
          raise ImportError(msg)
      try:
          return globals()[name]
      except KeyError:
          # This is turned into the appropriate ImportError
          raise AttributeError
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/datasets/__init__.py
imports: import textwrap
  from ._base import clear_data_home, get_data_home, load_breast_cancer, load_diabetes, load_digits, load_files, load_iris, load_linnerud, load_sample_image, load_sample_images, load_wine
  from ._california_housing import fetch_california_housing
  from ._covtype import fetch_covtype
  from ._kddcup99 import fetch_kddcup99
  from ._lfw import fetch_lfw_pairs, fetch_lfw_people
  from ._olivetti_faces import fetch_olivetti_faces
  from ._openml import fetch_openml
  from ._rcv1 import fetch_rcv1
  from ._samples_generator import make_biclusters, make_blobs, make_checkerboard, make_circles, make_classification, make_friedman1, make_friedman2, make_friedman3, make_gaussian_quantiles, make_hastie_10_2, make_low_rank_matrix, make_moons, make_multilabel_classification, make_regression, make_s_curve, make_sparse_coded_signal, make_sparse_spd_matrix, make_sparse_uncorrelated, make_spd_matrix, make_swiss_roll
  from ._species_distributions import fetch_species_distributions
  from ._svmlight_format_io import dump_svmlight_file, load_svmlight_file, load_svmlight_files
  from ._twenty_newsgroups import fetch_20newsgroups, fetch_20newsgroups_vectorized
label_desc: Use image node_id 1 for calling a global function with example usage: __getattr__(name) and returns: globals