code: def _load_arff_response(
      url: str,
      data_home: Optional[str],
      parser: str,
      output_type: str,
      openml_columns_info: dict,
      feature_names_to_select: List[str],
      target_names_to_select: List[str],
      shape: Optional[Tuple[int, int]],
      md5_checksum: str,
      n_retries: int = 3,
      delay: float = 1.0,
      read_csv_kwargs: Optional[Dict] = None,
  ):
      """Load the ARFF data associated with the OpenML URL.
  
      In addition of loading the data, this function will also check the
      integrity of the downloaded file from OpenML using MD5 checksum.
  
      Parameters
      ----------
      url : str
          The URL of the ARFF file on OpenML.
  
      data_home : str
          The location where to cache the data.
  
      parser : {"liac-arff", "pandas"}
          The parser used to parse the ARFF file.
  
      output_type : {"numpy", "pandas", "sparse"}
          The type of the arrays that will be returned. The possibilities are:
  
          - `"numpy"`: both `X` and `y` will be NumPy arrays;
          - `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array;
          - `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a
            pandas Series or DataFrame.
  
      openml_columns_info : dict
          The information provided by OpenML regarding the columns of the ARFF
          file.
  
      feature_names_to_select : list of str
          The list of the features to be selected.
  
      target_names_to_select : list of str
          The list of the target variables to be selected.
  
      shape : tuple or None
          With `parser="liac-arff"`, when using a generator to load the data,
          one needs to provide the shape of the data beforehand.
  
      md5_checksum : str
          The MD5 checksum provided by OpenML to check the data integrity.
  
      n_retries : int, default=3
          The number of times to retry downloading the data if it fails.
  
      delay : float, default=1.0
          The delay between two consecutive downloads in seconds.
  
      read_csv_kwargs : dict, default=None
          Keyword arguments to pass to `pandas.read_csv` when using the pandas parser.
          It allows to overwrite the default options.
  
          .. versionadded:: 1.3
  
      Returns
      -------
      X : {ndarray, sparse matrix, dataframe}
          The data matrix.
  
      y : {ndarray, dataframe, series}
          The target.
  
      frame : dataframe or None
          A dataframe containing both `X` and `y`. `None` if
          `output_array_type != "pandas"`.
  
      categories : list of str or None
          The names of the features that are categorical. `None` if
          `output_array_type == "pandas"`.
      """
      gzip_file = _open_openml_url(
          url, data_home, n_retries=n_retries, delay=delay
      )
      with closing(gzip_file):
          md5 = hashlib.md5()
          for chunk in iter(lambda: gzip_file.read(4096), b""):
              md5.update(chunk)
          actual_md5_checksum = md5.hexdigest()
  
      if actual_md5_checksum != md5_checksum:
          raise ValueError(
              f"md5 checksum of local file for {url} does not match description: "
              f"expected: {md5_checksum} but got {actual_md5_checksum}. "
              "Downloaded file could have been modified / corrupted, clean cache "
              "and retry..."
          )
  
      def _open_url_and_load_gzip_file(
          url, data_home, n_retries, delay, arff_params
      ):
          gzip_file = _open_openml_url(
              url, data_home, n_retries=n_retries, delay=delay
          )
          with closing(gzip_file):
              return load_arff_from_gzip_file(gzip_file, **arff_params)
  
      arff_params: Dict = dict(
          parser=parser,
          output_type=output_type,
          openml_columns_info=openml_columns_info,
          feature_names_to_select=feature_names_to_select,
          target_names_to_select=target_names_to_select,
          shape=shape,
          read_csv_kwargs=read_csv_kwargs or {},
      )
      try:
          X, y, frame, categories = _open_url_and_load_gzip_file(
              url, data_home, n_retries, delay, arff_params
          )
      except Exception as exc:
          if parser != "pandas":
              raise
  
          from pandas.errors import ParserError
  
          if not isinstance(exc, ParserError):
              raise
  
          # A parsing error could come from providing the wrong quotechar
          # to pandas. By default, we use a double quote. Thus, we retry
          # with a single quote before to raise the error.
          arff_params["read_csv_kwargs"].update(quotechar="'")
          X, y, frame, categories = _open_url_and_load_gzip_file(
              url, data_home, n_retries, delay, arff_params
          )
  
      return X, y, frame, categories
  
imports: import gzip
  import hashlib
  import json
  import os
  import shutil
  import time
  from contextlib import closing
  from functools import wraps
  from os.path import join
  from tempfile import TemporaryDirectory
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  from urllib.error import HTTPError, URLError
  from urllib.request import Request, urlopen
  from warnings import warn
  import numpy
  from ..utils import Bunch, check_pandas_support
  from ..utils._param_validation import Hidden, Integral, Interval, Real, StrOptions, validate_params
  from .None import get_data_home
  from ._arff_parser import load_arff_from_gzip_file
label_desc: Use image node_id 11 for calling a global function with example usage: _load_arff_response(url, data_home, parser, output_type, openml_columns_info, feature_names_to_select, target_names_to_select, shape, md5_checksum, n_retries, delay, read_csv_kwargs) and returns: X,  y,  frame,  categories, load_arff_from_gzip_file