code: def uncollect_if_test_explainer(**kwargs):
      """
      This function is used to skip combinations of explainers
      and classifiers that do not make sense. This is achieved
      by using the hooks in conftest.py. Such functions should
      be passed to the @pytest.mark.uncollect_if decorator as
      the func argument. They should take the same inputs as the
      test function. Because this test function is parametrized
      with a lazy fixture, in this case the arguments name change
      (ie explainer can be both at_iris_explainer or at_adult_explainer),
      the **kwargs argument is used to collect all inputs.
      """
  
      # both explainer and classifier fixtures are parametrized using a LazyFixture
      # object that has a name attribute, representing the name of the dataset fixture
      clf_dataset_name = kwargs["rf_classifier"].name.split("_")[
          0
      ]  # "adult_data" -> "adult"
      exp_dataset_name = kwargs["explainer"].name.split("_")[
          1
      ]  # "at_adult_explainer" -> "adult"
  
      conditions = [
          clf_dataset_name != exp_dataset_name,
      ]
  
      return any(conditions)
  
file_path: alibi/alibi/explainers/tests/test_anchor_tabular.py
imports: from collections import OrderedDict, defaultdict
  from copy import deepcopy
  import numpy
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from alibi.api.defaults import DEFAULT_META_ANCHOR, DEFAULT_DATA_ANCHOR
  from alibi.exceptions import NotFittedError, PredictorCallError, PredictorReturnTypeError
  from alibi.explainers import AnchorTabular, DistributedAnchorTabular
  from alibi.explainers.tests.utils import predict_fcn
label_desc: Use image node_id 1 for calling a global function with example usage: uncollect_if_test_explainer() and returns: any