code: def at_adult_explainer(adult_data, rf_classifier, request):
      """
      Instantiates and fits an AnchorTabular explainer for the Adult dataset.
      """
  
      # fit random forest classifier
      predict_type = request.param
      data = adult_data
      clf, preprocessor = rf_classifier
  
      # instantiate and fit explainer
      pred_fn = predict_fcn(predict_type, clf, preprocessor)
      explainer = AnchorTabular(
          pred_fn,
          data["metadata"]["feature_names"],
          categorical_names=data["metadata"]["category_map"],
      )
      explainer.fit(data["X_train"], disc_perc=(25, 50, 75))
  
      return data["X_test"], explainer, pred_fn, predict_type
  
imports: import pytest
  import logging
  import shap
  import spacy
  import numpy
  from sklearn.linear_model import LogisticRegression, LinearRegression
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from alibi.explainers import ALE
  from alibi.explainers import AnchorTabular
  from alibi.explainers import KernelShap, TreeShap
  from alibi.explainers.tests.utils import predict_fcn, MockTreeExplainer
  from alibi.tests.utils import MockPredictor
  from alibi.utils.lang_model import BertBaseUncased, DistilbertBaseUncased, RobertaBase
  from alibi.utils.download import spacy_model
  import tensorflow
  import alibi_testing
  from alibi_testing.data import get_adult_data, get_iris_data, get_diabetes_data, get_mnist_data, get_movie_sentiment_data
label_desc: Use image node_id 13 for calling a global function with example usage: at_adult_explainer(adult_data, rf_classifier, request) and returns: data,  explainer,  pred_fn,  predict_type