code: def run_api_explain_example(
      visualization=False, timeout=None, with_tuning=True
  ):
      train_data = pd.read_csv(
          f"{fedot_project_root()}/cases/data/cancer/cancer_train.csv",
          index_col=0,
      )
      figure_path = "api_explain_example.png"
  
      # Feature and class names for visualization
      feature_names = train_data.columns.tolist()
      target_name = feature_names.pop()
      target = train_data[target_name]
      class_names = target.unique().astype(str).tolist()
  
      # Building simple pipeline
      model = Fedot(
          problem="classification",
          timeout=timeout,
          with_tuning=with_tuning,
      )
      model.fit(
          features=train_data, target=target_name, predefined_model="rf"
      )
  
      # Current pipeline explaining
      explainer = model.explain(
          method="surrogate_dt",
          visualization=visualization,
          # The following parameters are only used if visualize == True:
          save_path=figure_path,
          dpi=200,
          feature_names=feature_names,
          class_names=class_names,
          precision=6,
      )
  
      return explainer
  
label_desc: Use image node_id 1 for calling a global function with example usage: run_api_explain_example(visualization, timeout, with_tuning) and returns: explainer