code: def fetch_credit_card(
      *, cache=True, data_home=None, as_frame=True, return_X_y=False
  ):
      """Load the 'Default of Credit Card clients' dataset (binary classification).
  
      ===============   ===============
      Samples total               30000
      Dimensionality                 23
      Features                     real
      Classes                         2
      ===============   ===============
  
      Source: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
      I-Cheng Yeh and Che-hui Lien, "The comparisons of data mining techniques for the
      predictive accuracy of probability of default of credit card clients", Expert Systems
      with Applications, 36(2), 2473-2480, 2009
  
      Parameters
      ----------
      cache : boolean, default=True
          Whether to cache downloaded datasets using joblib
  
      data_home : optional, default: None
          Specifiy another download and cache folder for the datasets. By default,
          all scikit-learn data is stored in '~/.fairlearn-data' subfolders.
  
      as_frame : boolean, default=True
          If True,
              Returns the data as Pandas DataFrame, and the target
              is returned as a Pandas Series.
          If False,
              Returns a scikit-learn Bunch object with ``frame`` attribute containing
              the data and the target.
  
          .. versionchanged:: 0.9.0
              Default value changed to True.
  
      return_X_y : boolean, default=False.
          If True,
              returns ``(data.data, data.target)``
          Else,
              return Sci-kit Learn Bunch object
  
  
      Returns
      -------
      dataset : class:`~sklearn.utils.Bunch`
          Dictionary-like object, with the following attributes.
  
          data : NumPy Array or Pandas DataFrame, Shape (30000, 23)
              Each row corresponds to the 23 feature values in order.
              If ``as_frame`` is True, ``data`` is a Pandas DataFrame
          target : NumPy Array or Pandas Series, Shape (30000,)
              Each value represents whether an applicant defaulted on credit loan.
              If ``as_frame`` is True, ``target`` is a Pandas Series.
          feature_names : List of Strings, Length 23
              Array of ordered feature names used in the dataset.
          DESCR : string
              Description of the UCI Default of Credit Card
          categories : dict or None
              Maps each categorical feature name to a list of values, such that the
              value encoded as i is ith in the list. If ``as_frame`` is True, this is None.
          frame : pandas DataFrame
              Only present when ``as_frame`` is True. DataFrame with ``data`` and ``target``.
  
      (data, target) : tuple if ``return_X_y`` is True
  
      Notes
      ----------
      Our API largely follows the API of :func:`sklearn.datasets.fetch_openml`.
  
      """
      if not data_home:
          data_home = Path().home() / _DOWNLOAD_DIRECTORY_NAME
  
      # For data_home see
      # https://github.com/scikit-learn/scikit-learn/issues/27447
      return fetch_openml(
          data_id=42477,
          data_home=str(data_home),
          cache=cache,
          as_frame=as_frame,
          return_X_y=return_X_y,
          parser="auto",
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_credit_card() and returns: fetch_openml