class_docstr: Abstract class for local forecasting models that are trained separately for each time series.
  
  Prediction is parallelized across CPU cores using joblib.Parallel.
  
  Attributes
  ----------
  allowed_local_model_args : List[str]
      Argument that can be passed to the underlying local model.
  default_n_jobs : Union[int, float]
      Default number of CPU cores used to train models. If float, this fraction of CPU cores will be used.
  default_max_ts_length : Optional[int]
      If not None, only the last ``max_ts_length`` time steps of each time series will be used to train the model.
      This significantly speeds up fitting and usually leads to no change in accuracy.
  init_time_in_seconds : int
      Time that it takes to initialize the model in seconds (e.g., because of JIT compilation by Numba).
      If time_limit is below this number, model won't be trained.
code: def _update_local_model_args(
      self, local_model_args: Dict[str, Any]
  ) -> Dict[str, Any]:
      return local_model_args
  
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 3 for calling the AbstractLocalModel obj's underlying member method code with example usage: obj._update_local_model_args(local_model_args) and returns: local_model_args