class_docstr: Baseline model that sets the forecast equal to the last observed value.
  
  Quantiles are obtained by assuming that the residuals follow zero-mean normal distribution, scale of which is
  estimated from the empirical distribution of the residuals.
  As described in https://otexts.com/fpp3/prediction-intervals.html
  
  Other Parameters
  ----------------
  n_jobs : int or float, default = 0.5
      Number of CPU cores used to fit the models in parallel.
      When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used.
      When set to a positive integer, that many cores are used.
      When set to -1, all CPU cores are used.
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code: def _predict_with_local_model(
      self,
      time_series: pd.Series,
      local_model_args: dict,
  ) -> pd.DataFrame:
      return seasonal_naive_forecast(
          target=time_series.values.ravel(),
          prediction_length=self.prediction_length,
          quantile_levels=self.quantile_levels,
          seasonal_period=1,
      )
  
file_path: autogluon/timeseries/src/autogluon/timeseries/models/local/naive.py
imports: from typing import Callable
  import numpy
  import pandas
  from autogluon.timeseries.models.local.abstract_local_model import AbstractLocalModel, seasonal_naive_forecast
label_desc: Use image node_id 1 for calling the NaiveModel obj's underlying member method code with example usage: obj._predict_with_local_model(time_series, local_model_args) and returns: seasonal_naive_forecast