code: def seasonal_naive_forecast(
      target: np.ndarray,
      prediction_length: int,
      quantile_levels: List[float],
      seasonal_period: int,
  ) -> pd.DataFrame:
      """Generate seasonal naive forecast, predicting the last observed value from the same period."""
      forecast = {}
      # At least seasonal_period + 2 values are required to compute sigma for seasonal naive
      if len(target) > seasonal_period + 1 and seasonal_period > 1:
          indices = [
              len(target) - seasonal_period + k % seasonal_period
              for k in range(prediction_length)
          ]
          forecast["mean"] = target[indices]
          residuals = (
              target[seasonal_period:] - target[:-seasonal_period]
          )
  
          sigma = np.sqrt(np.mean(np.square(residuals)))
          num_full_seasons = (
              np.arange(1, prediction_length + 1) // seasonal_period
          )
          sigma_per_timestep = sigma * np.sqrt(num_full_seasons + 1)
      else:
          # Fall back to naive forecast
          forecast["mean"] = np.full(
              shape=[prediction_length], fill_value=target[-1]
          )
          residuals = target[1:] - target[:-1]
  
          sigma = np.sqrt(np.mean(np.square(residuals)))
          sigma_per_timestep = sigma * np.sqrt(
              np.arange(1, prediction_length + 1)
          )
  
      for q in quantile_levels:
          forecast[str(q)] = (
              forecast["mean"] + norm.ppf(q) * sigma_per_timestep
          )
  
      return pd.DataFrame(forecast)
  
imports: import logging
  import time
  from multiprocessing import TimeoutError, cpu_count
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from scipy.stats import norm
  from autogluon.common.utils.log_utils import set_logger_verbosity
  from autogluon.core.utils.exceptions import TimeLimitExceeded
  from autogluon.timeseries.dataset.ts_dataframe import ITEMID, TimeSeriesDataFrame
  from autogluon.timeseries.models.abstract import AbstractTimeSeriesModel
  from autogluon.timeseries.utils.datetime import get_seasonality
  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 1 for calling a global function with example usage: seasonal_naive_forecast(target, prediction_length, quantile_levels, seasonal_period) and returns: pd