code: def rf_classifier(request):
      data = request.param
      is_preprocessor = False
      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
  
imports: import numbers
  import sys
  import numpy
  from numpy.testing import assert_allclose
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier
  import tempfile
  import tensorflow
  from typing import List, Union
  from alibi.explainers import ALE, AnchorImage, AnchorTabular, AnchorText, IntegratedGradients, KernelShap, TreeShap, CounterfactualRLTabular, GradientSimilarity
  from alibi.explainers.integrated_gradients import LayerState
  from alibi.saving import load_explainer
  from alibi_testing.data import get_adult_data, get_iris_data, get_movie_sentiment_data
  import alibi_testing
  from alibi.utils.download import spacy_model
  from alibi.explainers.tests.utils import predict_fcn
label_desc: Use image node_id 7 for calling a global function with example usage: rf_classifier(request) and returns: clf