code: def run_classification_multiobj_example(
      visualization=False, timeout=1, with_tuning=True
  ):
      train_data = pd.read_csv(
          f"{fedot_project_root()}/examples/data/Hill_Valley_with_noise_Training.data"
      )
      test_data = pd.read_csv(
          f"{fedot_project_root()}/examples/data/Hill_Valley_with_noise_Testing.data"
      )
      target = test_data["class"]
      del test_data["class"]
      problem = "classification"
  
      metric_names = ["f1", "node_number"]
      auto_model = Fedot(
          problem=problem,
          timeout=timeout,
          preset="best_quality",
          metric=metric_names,
          with_tuning=with_tuning,
      )
      auto_model.fit(features=train_data, target="class")
      prediction = auto_model.predict_proba(features=test_data)
      print(auto_model.get_metrics(target))
  
      if visualization:
          auto_model.plot_prediction()
          auto_model.plot_pareto()
  
      return prediction
  
imports: import pandas
  from fedot import Fedot
  from fedot.core.utils import fedot_project_root
  from fedot.core.utils import set_random_seed
label_desc: Use image node_id 1 for calling a global function with example usage: run_classification_multiobj_example(visualization, timeout, with_tuning) and returns: prediction