code: def launch_fedot_forecasting(
      target_column: int = 1,
      forecast_horizon: int = 50,
      number_of_series_to_use: int = 25,
  ):
      """Example how to launch FEDOT AutmoML for multivariate forecasting"""
      path_to_file = (
          fedot_project_root()
          / "cases"
          / "data"
          / "multivariate_ssh.csv"
      )
      df = pd.read_csv(path_to_file, parse_dates=["datetime"])
      train_df, test_df = train_test_split(df, forecast_horizon)
  
      # Prepare train data in a form of dictionary {'time series label': numpy array}
      train_data = {}
      feature_series = list(train_df["label"].unique())
      for feature_ts in feature_series[:number_of_series_to_use]:
          current_ts = train_df[train_df["label"] == feature_ts]
          train_data.update(
              {str(feature_ts): np.array(current_ts["value"])}
          )
  
      # Configure AutoML
      task_parameters = TsForecastingParams(
          forecast_length=forecast_horizon
      )
      model = Fedot(
          problem="ts_forecasting",
          task_params=task_parameters,
          timeout=0.5,
          cv_folds=2,
          with_tuning=False,
          seed=0,
      )
      target_series = train_df[train_df["label"] == target_column]
      obtained_pipeline = model.fit(
          features=train_data, target=np.array(target_series["value"])
      )
  
      obtained_pipeline.show()
  
      # Use historical value to make forecast
      forecast = model.predict(train_data)
  
      plot_results(df, target_column, forecast, forecast_horizon)
  
file_path: FEDOT/cases/multivariate_ts_forecasting.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  from fedot import Fedot
  from fedot.core.repository.tasks import TsForecastingParams
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 3 for calling a global function with example usage: launch_fedot_forecasting(target_column, forecast_horizon, number_of_series_to_use) without return types