code: def adult():
      """
      Download "Adult Data Set" [1] from UCI Machine Learning Repository.
  
      Will return two pandas.DataFrame-s, first with train part (adult.data) and second with test part
      (adult.test) of the dataset.
  
      [1]: https://archive.ics.uci.edu/ml/datasets/Adult
      """
      # via https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names
      names = (
          "age",
          "workclass",
          "fnlwgt",
          "education",
          "education-num",
          "marital-status",
          "occupation",
          "relationship",
          "race",
          "sex",
          "capital-gain",
          "capital-loss",
          "hours-per-week",
          "native-country",
          "income",
      )
      dtype = {
          "age": float,
          "workclass": object,
          "fnlwgt": float,
          "education": object,
          "education-num": float,
          "marital-status": object,
          "occupation": object,
          "relationship": object,
          "race": object,
          "sex": object,
          "capital-gain": float,
          "capital-loss": float,
          "hours-per-week": float,
          "native-country": object,
          "income": object,
      }
  
      # proxy.sandbox.yandex-team.ru is Yandex internal storage, we first try to download it from
      # internal storage to avoid putting too much pressure on UCI storage from our internal CI
  
      train_urls = (
          "https://proxy.sandbox.yandex-team.ru/779118052",
          "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
      )
      train_md5 = "5d7c39d7b8804f071cdd1f2a7c460872"
      train_path = tempfile.mktemp()
      _cached_download(train_urls, train_md5, train_path)
  
      test_urls = (
          "https://proxy.sandbox.yandex-team.ru/779120000",
          "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",
      )
      test_md5 = "35238206dfdf7f1fe215bbb874adecdc"
      test_path = tempfile.mktemp()
      _cached_download(test_urls, test_md5, test_path)
  
      train_df = pd.read_csv(
          train_path,
          names=names,
          header=None,
          sep=r",\s*",
          na_values=["?"],
          engine="python",
      )
      os.remove(train_path)
  
      # lines in test part end with dot, thus we need to fix last column of the dataset
      test_df = pd.read_csv(
          test_path,
          names=names,
          header=None,
          sep=r",\s*",
          na_values=["?"],
          skiprows=1,
          converters={"income": lambda x: x[:-1]},
          engine="python",
      )
      os.remove(test_path)
  
      # pandas 0.19.1 doesn't support `dtype` parameter for `read_csv` when `python` engine is used,
      # so we have to do the casting manually; also we can't use `converters` together with `dtype`
      train_df = train_df.astype(dtype)
      test_df = test_df.astype(dtype)
  
      return train_df, test_df
  
imports: import hashlib
  import logging
  import numpy
  import os
  import pandas
  import tarfile
  import tempfile
  import six
  import shutil
  from .core import PATH_TYPES, fspath
label_desc: Use image node_id 19 for calling a global function with example usage: adult() and returns: train_df,  test_df