code: def test_fetch_openml_consistency_parser(monkeypatch, data_id):
      """Check the consistency of the LIAC-ARFF and pandas parsers."""
      pd = pytest.importorskip("pandas")
  
      _monkey_patch_webbased_functions(
          monkeypatch, data_id, gzip_response=True
      )
      bunch_liac = fetch_openml(
          data_id=data_id,
          as_frame=True,
          cache=False,
          parser="liac-arff",
      )
      bunch_pandas = fetch_openml(
          data_id=data_id,
          as_frame=True,
          cache=False,
          parser="pandas",
      )
  
      # The data frames for the input features should match up to some numerical
      # dtype conversions (e.g. float64 <=> Int64) due to limitations of the
      # LIAC-ARFF parser.
      data_liac, data_pandas = bunch_liac.data, bunch_pandas.data
  
      def convert_numerical_dtypes(series):
          pandas_series = data_pandas[series.name]
          if pd.api.types.is_numeric_dtype(pandas_series):
              return series.astype(pandas_series.dtype)
          else:
              return series
  
      data_liac_with_fixed_dtypes = data_liac.apply(
          convert_numerical_dtypes
      )
      pd.testing.assert_frame_equal(
          data_liac_with_fixed_dtypes, data_pandas
      )
  
      # Let's also check that the .frame attributes also match
      frame_liac, frame_pandas = bunch_liac.frame, bunch_pandas.frame
  
      # Note that the .frame attribute is a superset of the .data attribute:
      pd.testing.assert_frame_equal(
          frame_pandas[bunch_pandas.feature_names], data_pandas
      )
  
      # However the remaining columns, typically the target(s), are not necessarily
      # dtyped similarly by both parsers due to limitations of the LIAC-ARFF parser.
      # Therefore, extra dtype conversions are required for those columns:
  
      def convert_numerical_and_categorical_dtypes(series):
          pandas_series = frame_pandas[series.name]
          if pd.api.types.is_numeric_dtype(pandas_series):
              return series.astype(pandas_series.dtype)
          elif isinstance(pandas_series.dtype, pd.CategoricalDtype):
              # Compare categorical features by converting categorical liac uses
              # strings to denote the categories, we rename the categories to make
              # them comparable to the pandas parser. Fixing this behavior in
              # LIAC-ARFF would allow to check the consistency in the future but
              # we do not plan to maintain the LIAC-ARFF on the long term.
              return series.cat.rename_categories(
                  pandas_series.cat.categories
              )
          else:
              return series
  
      frame_liac_with_fixed_dtypes = frame_liac.apply(
          convert_numerical_and_categorical_dtypes
      )
      pd.testing.assert_frame_equal(
          frame_liac_with_fixed_dtypes, frame_pandas
      )
  
imports: import gzip
  import json
  import os
  import re
  from functools import partial
  from importlib import resources
  from io import BytesIO
  from urllib.error import HTTPError
  import numpy
  import pytest
  import scipy.sparse
  import sklearn
  from sklearn import config_context
  from sklearn.datasets import fetch_openml
  from sklearn.datasets._openml import _OPENML_PREFIX, _get_local_path, _open_openml_url, _retry_with_clean_cache
  from sklearn.utils import Bunch, check_pandas_support
  from sklearn.utils._testing import SkipTest, assert_allclose, assert_array_equal, fails_if_pypy
label_desc: Use image node_id 4 for calling a global function with example usage: test_fetch_openml_consistency_parser(monkeypatch, data_id) and returns: series, series, series, series, series