code: def rf_classifier(request):
      """
      Trains a random forest classifier. Because it is scoped
      at module level, the state of this  fixture should not be
      mutated during test - if you need to do so, please copy the
      objects returned.
      """
  
      is_preprocessor = False
      preprocessor = None
      # this fixture should be parametrised with a fixture that
      # returns a dataset dictionary with specified attributes
      # see test_anchor_tabular for a usage example
      data = request.param
  
      if data["preprocessor"]:
          is_preprocessor = True
          preprocessor = data["preprocessor"]
  
      np.random.seed(0)
      clf = RandomForestClassifier(n_estimators=50)
  
      if is_preprocessor:
          clf.fit(
              preprocessor.transform(data["X_train"]), data["y_train"]
          )
      else:
          clf.fit(data["X_train"], data["y_train"])
  
      return clf, preprocessor
  
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 7 for calling a global function with example usage: rf_classifier(request) and returns: clf,  preprocessor