code: def load_csv_data(
      data_file_name,
      *,
      data_module=DATA_MODULE,
      descr_file_name=None,
      descr_module=DESCR_MODULE,
  ):
      """Loads `data_file_name` from `data_module with `importlib.resources`.
  
      Parameters
      ----------
      data_file_name : str
          Name of csv file to be loaded from `data_module/data_file_name`.
          For example `'wine_data.csv'`.
  
      data_module : str or module, default='sklearn.datasets.data'
          Module where data lives. The default is `'sklearn.datasets.data'`.
  
      descr_file_name : str, default=None
          Name of rst file to be loaded from `descr_module/descr_file_name`.
          For example `'wine_data.rst'`. See also :func:`load_descr`.
          If not None, also returns the corresponding description of
          the dataset.
  
      descr_module : str or module, default='sklearn.datasets.descr'
          Module where `descr_file_name` lives. See also :func:`load_descr`.
          The default is `'sklearn.datasets.descr'`.
  
      Returns
      -------
      data : ndarray of shape (n_samples, n_features)
          A 2D array with each row representing one sample and each column
          representing the features of a given sample.
  
      target : ndarry of shape (n_samples,)
          A 1D array holding target variables for all the samples in `data`.
          For example target[0] is the target variable for data[0].
  
      target_names : ndarry of shape (n_samples,)
          A 1D array containing the names of the classifications. For example
          target_names[0] is the name of the target[0] class.
  
      descr : str, optional
          Description of the dataset (the content of `descr_file_name`).
          Only returned if `descr_file_name` is not None.
      """
      with _open_text(data_module, data_file_name) as csv_file:
          data_file = csv.reader(csv_file)
          temp = next(data_file)
          n_samples = int(temp[0])
          n_features = int(temp[1])
          target_names = np.array(temp[2:])
          data = np.empty((n_samples, n_features))
          target = np.empty((n_samples,), dtype=int)
  
          for i, ir in enumerate(data_file):
              data[i] = np.asarray(ir[:-1], dtype=np.float64)
              target[i] = np.asarray(ir[-1], dtype=int)
  
      if descr_file_name is None:
          return data, target, target_names
      else:
          assert descr_module is not None
          descr = load_descr(
              descr_module=descr_module, descr_file_name=descr_file_name
          )
          return data, target, target_names, descr
  
imports: import csv
  import gzip
  import hashlib
  import os
  import shutil
  from collections import namedtuple
  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
  from ..utils.fixes import _contents, _open_binary, _open_text, _read_text
label_desc: Use image node_id 5 for calling a global function with example usage: load_csv_data(data_file_name) and returns: data,  target,  target_names, data,  target,  target_names,  descr