code: def load_linnerud(*, return_X_y=False, as_frame=False):
      """Load and return the physical exercise Linnerud dataset.
  
      This dataset is suitable for multi-output regression tasks.
  
      ==============   ============================
      Samples total    20
      Dimensionality   3 (for both data and target)
      Features         integer
      Targets          integer
      ==============   ============================
  
      Read more in the :ref:`User Guide <linnerrud_dataset>`.
  
      Parameters
      ----------
      return_X_y : bool, 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
  
      as_frame : bool, default=False
          If True, the data is a pandas DataFrame including columns with
          appropriate dtypes (numeric, string or categorical). The target is
          a pandas DataFrame or Series depending on the number of target columns.
          If `return_X_y` is True, then (`data`, `target`) will be pandas
          DataFrames or Series as described below.
  
          .. versionadded:: 0.23
  
      Returns
      -------
      data : :class:`~sklearn.utils.Bunch`
          Dictionary-like object, with the following attributes.
  
          data : {ndarray, dataframe} of shape (20, 3)
              The data matrix. If `as_frame=True`, `data` will be a pandas
              DataFrame.
          target: {ndarray, dataframe} of shape (20, 3)
              The regression targets. If `as_frame=True`, `target` will be
              a pandas DataFrame.
          feature_names: list
              The names of the dataset columns.
          target_names: list
              The names of the target columns.
          frame: DataFrame of shape (20, 6)
              Only present when `as_frame=True`. DataFrame with `data` and
              `target`.
  
              .. versionadded:: 0.23
          DESCR: str
              The full description of the dataset.
          data_filename: str
              The path to the location of the data.
          target_filename: str
              The path to the location of the target.
  
              .. versionadded:: 0.20
  
      (data, target) : tuple if ``return_X_y`` is True
          Returns a tuple of two ndarrays or dataframe of shape
          `(20, 3)`. Each row represents one sample and each column represents the
          features in `X` and a target in `y` of a given sample.
  
          .. versionadded:: 0.18
      """
      data_filename = "linnerud_exercise.csv"
      target_filename = "linnerud_physiological.csv"
  
      data_module_path = resources.files(DATA_MODULE)
      # Read header and data
      data_path = data_module_path / data_filename
      with data_path.open("r", encoding="utf-8") as f:
          header_exercise = f.readline().split()
          f.seek(0)  # reset file obj
          data_exercise = np.loadtxt(f, skiprows=1)
  
      target_path = data_module_path / target_filename
      with target_path.open("r", encoding="utf-8") as f:
          header_physiological = f.readline().split()
          f.seek(0)  # reset file obj
          data_physiological = np.loadtxt(f, skiprows=1)
  
      fdescr = load_descr("linnerud.rst")
  
      frame = None
      if as_frame:
          (
              frame,
              data_exercise,
              data_physiological,
          ) = _convert_data_dataframe(
              "load_linnerud",
              data_exercise,
              data_physiological,
              header_exercise,
              header_physiological,
          )
      if return_X_y:
          return data_exercise, data_physiological
  
      return Bunch(
          data=data_exercise,
          feature_names=header_exercise,
          target=data_physiological,
          target_names=header_physiological,
          frame=frame,
          DESCR=fdescr,
          data_filename=data_filename,
          target_filename=target_filename,
          data_module=DATA_MODULE,
      )
  
imports: import csv
  import gzip
  import hashlib
  import os
  import shutil
  from collections import namedtuple
  from importlib import resources
  from numbers import Integral
  from os import environ, listdir, makedirs
  from os.path import expanduser, isdir, join, splitext
  from pathlib import Path
  from urllib.request import urlretrieve
  import numpy
  from ..preprocessing import scale
  from ..utils import Bunch, check_pandas_support, check_random_state
  from ..utils._param_validation import Interval, StrOptions, validate_params
label_desc: Use image node_id 13 for calling a global function with example usage: load_linnerud() and returns: Bunch, data_exercise,  data_physiological