code: def build_pred_ints(start=5000, end=7000, horizon=200):
      d = pd.read_csv(
          f"{fedot_project_root()}/examples/data/ts/ts_long.csv"
      )
      init_series = d[d["series_id"] == "temp"]["value"].to_numpy()
  
      ts = init_series[start:end]
      ts_test = init_series[end : end + horizon]
  
      fig, ax = plt.subplots()
      ax.plot(range(len(ts)), ts)
      ax.plot(range(len(ts), len(ts) + len(ts_test)), ts_test)
  
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=horizon),
      )
      idx = np.arange(len(ts))
      train_input = InputData(
          idx=idx,
          features=ts,
          target=ts,
          task=task,
          data_type=DataTypesEnum.ts,
      )
      model = Fedot(
          problem="ts_forecasting",
          task_params=task.task_params,
          timeout=3,
          preset="ts",
          show_progress=False,
      )
  
      model.fit(train_input)
      model.forecast()
  
      # initilize PredictionIntervals instance
      params = PredictionIntervalsParams(
          number_mutations=50,
          show_progress=False,
          mutations_choice="different",
      )
      pred_ints = PredictionIntervals(
          model=model,
          horizon=horizon,
          method="mutation_of_best_pipeline",
          params=params,
      )
  
      pred_ints.fit(train_input)
  
      x = pred_ints.forecast()
      pred_ints.plot(ts_test=ts_test)
      pred_ints.get_base_quantiles(train_input)
      pred_ints.plot_base_quantiles()
  
      # Evaluate results using metrcis picp (predicition interval coverage probability) and interval_score,
      # see  https://arxiv.org/pdf/2007.05709.pdf
  
      print(
          f"""Evaluate results using metrcis picp (predicition interval coverage probability) and interval_score,
  see https://arxiv.org/pdf/2007.05709.pdf
  interval_score: {interval_score(ts_test,up=x['up_int'],low=x['low_int'])}
  picp: {picp(ts_test,low = x['low_int'],up=x['up_int'])}
  """
      )
  
file_path: FEDOT/examples/advanced/time_series_forecasting/prediction_intervals.py
imports: import numpy
  import pandas
  import matplotlib.pyplot
  from fedot import Fedot
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.data.data import InputData
  from fedot.core.repository.tasks import TsForecastingParams, Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
  from fedot.core.pipelines.prediction_intervals.main import PredictionIntervals
  from fedot.core.pipelines.prediction_intervals.params import PredictionIntervalsParams
  from fedot.core.pipelines.prediction_intervals.metrics import interval_score, picp
label_desc: Use image node_id 1 for calling a global function with example usage: build_pred_ints(start, end, horizon) without return types