code: def test_explainer(
      text,
      n_punctuation_marks,
      n_unique_words,
      lr_classifier,
      predict_type,
      anchor,
      use_proba,
      sampling_strategy,
      filling,
      threshold,
      lang_model,
      nlp,
  ):
      # test parameters
      sample_proba = 0.5  # probability of masking a word
      top_n = 500  # use top 500 words
      temperature = 1  # temperature coeff
      num_samples = 100  # number of sampled
      frac_mask_templates = 0.1  # fraction of masking templates
      n_covered_ex = 5  # number of examples where the anchor applies to be returned
  
      # fit and initialize predictor
      clf, preprocessor = lr_classifier
      predictor = predict_fcn(predict_type, clf, preproc=preprocessor)
  
      # setup explainer
      perturb_opts = {
          "filling": filling,
          "sample_proba": sample_proba,
          "temperature": temperature,
          "top_n": top_n,
          "frac_mask_templates": frac_mask_templates,
          "use_proba": use_proba,
      }
  
      # test explainer initialization
      explainer = AnchorText(
          predictor=predictor,
          sampling_strategy=sampling_strategy,
          nlp=nlp,
          language_model=lang_model,
          **perturb_opts,
      )
      assert explainer.predictor(["book"]).shape == (1,)
  
      # set sampler
      explainer.n_covered_ex = n_covered_ex
      explainer.perturbation.set_text(text=text)
  
      # set instance label
      label = (
          np.argmax(predictor([text])[0])
          if predict_type == "proba"
          else predictor([text])[0]
      )
      explainer.instance_label = label
  
      # check punctuation and words for `unknown` and `similarity` sampling
      if sampling_strategy in [
          AnchorText.SAMPLING_UNKNOWN,
          AnchorText.SAMPLING_SIMILARITY,
      ]:
          assert (
              len(explainer.perturbation.punctuation)
              == n_punctuation_marks
          )
          assert len(explainer.perturbation.words) == len(
              explainer.perturbation.positions
          )
      else:
          # do something similar for the transformers. this simplified version
          # works because there are not multiple consecutive punctuations
          tokens = lang_model.tokenizer.tokenize(text)
          punctuation = []
  
          for token in tokens:
              if lang_model.is_punctuation(token, string.punctuation):
                  punctuation.append(token.strip())
  
          # select all words without punctuation
          words = []
  
          for i, token in enumerate(tokens):
              if (not lang_model.is_subword_prefix(token)) and (
                  not lang_model.is_punctuation(
                      token, string.punctuation
                  )
              ):
                  word = lang_model.select_word(
                      tokens, i, string.punctuation
                  )
                  words.append(word.strip())
  
          # set with all unique words including punctuation
          unique_words = set(words) | set(punctuation)
          assert len(punctuation) == n_punctuation_marks
          assert (
              len(unique_words) >= n_unique_words - 1
          )  # (because of Roberta first token)
  
      # test sampler
      (
          cov_true,
          cov_false,
          labels,
          data,
          coverage,
          _,
      ) = explainer.sampler((0, anchor), num_samples)
      if not anchor:
          assert coverage == -1
  
      # check that words in present are in the proposed anchor
      if (
          sampling_strategy in [AnchorText.SAMPLING_SIMILARITY]
      ) and len(anchor) > 0:
          assert len(anchor) * data.shape[0] == data[:, anchor].sum()
  
      if sampling_strategy == AnchorText.SAMPLING_UNKNOWN:
          all_words = explainer.perturbation.words
          assert len(np.unique(all_words)) == n_unique_words
  
      # test explanation
      explanation = explainer.explain(
          text, threshold=threshold, coverage_samples=100
      )
      assert explanation.precision >= threshold
      assert explanation.raw["prediction"].item() == label
      assert explanation.meta.keys() == DEFAULT_META_ANCHOR.keys()
      assert explanation.data.keys() == DEFAULT_DATA_ANCHOR.keys()
  
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 2 for calling a global function with example usage: test_explainer(text, n_punctuation_marks, n_unique_words, lr_classifier, predict_type, anchor, use_proba, sampling_strategy, filling, threshold, lang_model, nlp) without return types