code: def run_tpot_vs_fedot_example(
      train_file_path: str, test_file_path: str
  ):
      train_data = InputData.from_csv(train_file_path)
      test_data = InputData.from_csv(test_file_path)
  
      training_features = train_data.features
      testing_features = test_data.features
      training_target = train_data.target
      testing_target = test_data.target
  
      # Average CV score on the training set was: 0.93755
      exported_pipeline = make_pipeline(
          StackingEstimator(estimator=BernoulliNB()),
          RandomForestClassifier(),
      )
      # Fix random state for all the steps in exported pipeline
      set_param_recursive(exported_pipeline.steps, "random_state", 1)
  
      exported_pipeline.fit(training_features, training_target)
      results = exported_pipeline.predict_proba(testing_features)[:, 1]
  
      roc_auc_value = roc_auc(y_true=testing_target, y_score=results)
  
      print(f"ROC AUC for TPOT: {roc_auc_value}")
  
      node_scaling = PipelineNode("scaling")
      node_bernb = PipelineNode("bernb", nodes_from=[node_scaling])
      node_rf = PipelineNode(
          "rf", nodes_from=[node_bernb, node_scaling]
      )
      pipeline = Pipeline(node_rf)
  
      pipeline.fit(train_data)
      results = pipeline.predict(test_data)
  
      roc_auc_value = roc_auc(
          y_true=testing_target, y_score=results.predict
      )
      print(f"ROC AUC for FEDOT: {roc_auc_value}")
  
      return roc_auc_value
  
file_path: FEDOT/examples/advanced/automl/tpot_vs_fedot.py
imports: from fedot.core.data.data import InputData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from tpot.export_utils import set_param_recursive
  from tpot.builtins import StackingEstimator
  from sklearn.pipeline import make_pipeline
  from sklearn.naive_bayes import BernoulliNB
  from sklearn.metrics import roc_auc_score
  from sklearn.ensemble import RandomForestClassifier
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: run_tpot_vs_fedot_example(train_file_path, test_file_path) and returns: roc_auc_value