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.
      """
  
      sampling_strategy = kwargs["sampling_strategy"]
      lang_model = kwargs["lang_model"]
  
      cond1 = (sampling_strategy != "language_model") and (
          lang_model != ""
      )
      cond2 = (sampling_strategy == "language_model") and (
          lang_model == ""
      )
      return any([cond1, cond2])
  
imports: import pytest
  from pytest_lazyfixture import lazy_fixture
  import string
  import numpy
  from typing import List
  from alibi.api.defaults import DEFAULT_META_ANCHOR, DEFAULT_DATA_ANCHOR
  from alibi.exceptions import PredictorCallError, PredictorReturnTypeError
  from alibi.explainers import AnchorText
  from alibi.explainers.anchors.text_samplers import Neighbors, load_spacy_lexeme_prob
  from alibi.explainers.anchors.language_model_text_sampler import LanguageModelSampler
  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