code: def fetch_diabetes_hospital(
      *, as_frame=True, cache=True, data_home=None, return_X_y=False
  ):
      """Load the preprocessed Diabetes 130-Hospitals dataset (binary classification).
  
      Download it if necessary.
  
      ==============   ============================
      Samples total                          101766
      Dimensionality                             24
      Features         numeric, categorical, string
      Classes                                     2
      ==============   ============================
  
      Source: UCI Repository :footcite:`strack2014diabetes`
      Paper: Strack et al., 2014 :footcite:`strack2014impact`
  
      The "Diabetes 130-Hospitals" dataset represents 10 years of clinical care at 130
      U.S. hospitals and delivery networks, collected from 1999 to 2008. Each record
      represents the hospital admission record for a patient diagnosed with diabetes
      whose stay lasted between one to fourteen days.
  
      The original "Diabetes 130-Hospitals" dataset was collected by Beata Strack,
      Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura,
      Krzysztof J. Cios, and John N. Clore in 2014.
  
      This version of the dataset was derived by the Fairlearn team for the SciPy
      2021 tutorial "Fairness in AI Systems: From social context to practice using
      Fairlearn". In this version, the target variable "readmitted" is binarized
      into whether the patient was re-admitted within thirty days. The full
      pre-processing script is available
      `here <https://github.com/fairlearn/talks/blob/main/2021_scipy_tutorial/preprocess.py>`_.
  
      Read more in the :ref:`User Guide <diabetes_hospital_data>`.
  
      .. versionadded:: 0.8.0
  
      Parameters
      ----------
      as_frame : bool, default=True
          If True, the data is a pandas DataFrame including columns with
          appropriate dtypes (numeric, string or categorical).
  
          .. note::
              If set to False, this will raise an exception because of a type mismatch
              in the OpenML dataset.
  
          .. versionadded:: 0.9.0
  
      cache : bool, default=True
          Whether to cache downloaded datasets using joblib.
  
      data_home : str, default=None
          Specify another download and cache folder for the datasets.
          By default, all fairlearn data is stored in '~/.fairlearn-data'
          subfolders.
  
      return_X_y : bool, default=False
          If True, returns ``(data.data, data.target)`` instead of a Bunch
          object.
  
      Returns
      -------
      dataset : :obj:`~sklearn.utils.Bunch`
          Dictionary-like object, with the following attributes:
  
          data : ndarray, shape (101766, 24)
              Each row corresponding to the 24 feature values in order.
              If ``as_frame`` is True, ``data`` is a pandas object.
          target : numpy array of shape (101766,)
              Each value represents whether readmission of the patient
              occurred within 30 days of the release.
          feature_names : list of length 24
              Array of ordered feature names used in the dataset.
          DESCR : string
              Description of the Diabetes 130-Hospitals dataset.
          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`.
  
      References
      ----------
      .. footbibliography::
  
      """
      if not data_home:
          data_home = pathlib.Path().home() / _DOWNLOAD_DIRECTORY_NAME
  
      # For data_home see
      # https://github.com/scikit-learn/scikit-learn/issues/27447
      return fetch_openml(
          data_id=43874,
          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_diabetes_hospital() and returns: fetch_openml