code: def run_regression_example(
      visualise: bool = False,
      with_tuning: bool = True,
      timeout: float = 2.0,
      preset: str = "auto",
  ):
      data_path = f"{fedot_project_root()}/cases/data/cholesterol/cholesterol.csv"
  
      data = InputData.from_csv(
          data_path, task=Task(TaskTypesEnum.regression)
      )
      train, test = train_test_data_setup(data)
      problem = "regression"
  
      composer_params = {
          "history_dir": "custom_history_dir",
          "preset": preset,
      }
      auto_model = Fedot(
          problem=problem,
          seed=42,
          timeout=timeout,
          logging_level=logging.FATAL,
          with_tuning=with_tuning,
          **composer_params,
      )
  
      auto_model.fit(features=train, target="target")
      prediction = auto_model.predict(features=test)
      if visualise:
          auto_model.history.save("saved_regression_history.json")
          auto_model.plot_prediction()
      print(auto_model.get_metrics())
      return prediction
  
imports: import logging
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.repository.tasks import TaskTypesEnum, Task
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 1 for calling a global function with example usage: run_regression_example(visualise, with_tuning, timeout, preset) and returns: prediction