code: def __init__(
      self,
      freq: Optional[str] = None,
      prediction_length: int = 1,
      path: Optional[str] = None,
      name: Optional[str] = None,
      eval_metric: str = None,
      hyperparameters: Dict[str, Any] = None,
      **kwargs,  # noqa
  ):
      super().__init__(
          path=path,
          freq=freq,
          prediction_length=prediction_length,
          name=name,
          eval_metric=eval_metric,
          hyperparameters=hyperparameters,
          **kwargs,
      )
      from mlforecast import MLForecast
      from mlforecast.target_transforms import BaseTargetTransform
  
      self._sum_of_differences: int = 0  # number of time steps removed from each series by differencing
      self._max_ts_length: Optional[int] = None
      self._target_lags: Optional[List[int]] = None
      self._date_features: Optional[List[str]] = None
      self._mlf: Optional[MLForecast] = None
      self._scaler: Optional[BaseTargetTransform] = None
      self._residuals_std_per_item: Optional[pd.Series] = None
      self._avg_residuals_std: Optional[float] = None
  
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 1 to create a new AbstractMLForecastModel object from inherited base classes: AbstractTimeSeriesModel with example: obj = AbstractMLForecastModel(freq, prediction_length, path, name, eval_metric, hyperparameters)