docstr: Add quantile levels assuming that residuals follow normal distribution
code: def _add_gaussian_quantiles(
      self, predictions: pd.DataFrame, repeated_item_ids: pd.Series
  ):
      """
      Add quantile levels assuming that residuals follow normal distribution
      """
      from scipy.stats import norm
  
      scale_per_item = self._get_scale_per_item(
          repeated_item_ids.unique()
      )
      num_items = int(len(predictions) / self.prediction_length)
      sqrt_h = np.sqrt(np.arange(1, self.prediction_length + 1))
      # Series where normal_scale_per_timestep.loc[item_id].loc[N] = sqrt(1 + N) for N in range(prediction_length)
      normal_scale_per_timestep = pd.Series(
          np.tile(sqrt_h, num_items), index=repeated_item_ids
      )
  
      residuals_std_per_timestep = self._residuals_std_per_item.reindex(
          repeated_item_ids
      )
      # Use avg_residuals_std in case unseen item received for prediction
      if residuals_std_per_timestep.isna().any():
          residuals_std_per_timestep = (
              residuals_std_per_timestep.fillna(
                  value=self._avg_residuals_std
              )
          )
      std_per_timestep = (
          residuals_std_per_timestep
          * scale_per_item
          * normal_scale_per_timestep
      )
      for q in self.quantile_levels:
          predictions[str(q)] = (
              predictions["mean"]
              + norm.ppf(q) * std_per_timestep.to_numpy()
          )
      return predictions
  
imports: import logging
  import math
  import os
  import time
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
  import autogluon.core
  from autogluon.common.utils.log_utils import set_logger_verbosity
  from autogluon.tabular import TabularPredictor
  from autogluon.timeseries.dataset.ts_dataframe import ITEMID, TIMESTAMP, TimeSeriesDataFrame
  from autogluon.timeseries.models.abstract import AbstractTimeSeriesModel
  from autogluon.timeseries.models.local import SeasonalNaiveModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_ts_dataframe
  from autogluon.timeseries.utils.warning_filters import warning_filter
label_desc: Use image node_id 13 for calling the AbstractMLForecastModel obj's underlying member method code with example usage: obj._add_gaussian_quantiles(predictions, repeated_item_ids) and returns: predictions