code: def run_multi_output_case(path, vis=False):
      """Function launch case for river levels prediction on Lena river as
      multi-output regression task
  
      :param path: path to the file with table
      :param vis: is it needed to visualise pipeline and predictions
      """
      target_columns = [
          "1_day",
          "2_day",
          "3_day",
          "4_day",
          "5_day",
          "6_day",
          "7_day",
      ]
  
      data = InputData.from_csv(
          path, target_columns=target_columns, columns_to_drop=["date"]
      )
      train, test = train_test_data_setup(data)
  
      problem = "regression"
  
      automl_model = Fedot(problem=problem)
      automl_model.fit(features=train)
      predicted_array = automl_model.predict(features=test)
  
      # Convert output into one dimensional array
      forecast = np.ravel(predicted_array)
  
      mae_value = mean_absolute_error(np.ravel(test.target), forecast)
      print(f"MAE - {mae_value:.2f}")
  
      if vis:
          plot_predictions(predicted_array, test)
  
file_path: FEDOT/cases/multi_target_levels_forecasting.py
imports: import warnings
  import numpy
  from matplotlib import pyplot
  from sklearn.metrics import mean_absolute_error
  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.utils import fedot_project_root
label_desc: Use image node_id 4 for calling a global function with example usage: run_multi_output_case(path, vis) without return types