code: def _get_mlforecast_init_args(
      self, train_data: TimeSeriesDataFrame, model_params: dict
  ) -> dict:
      # TODO: Support lag generation for all pandas frequencies
      # TODO: Support date_feature generation for all pandas frequencies
      from mlforecast.target_transforms import Differences
  
      from .utils import MeanAbsScaler, StandardScaler
  
      lags = model_params.get("lags")
      if lags is None:
          lags = get_lags_for_frequency(self.freq)
      self._target_lags = np.array(sorted(set(lags)), dtype=np.int64)
  
      date_features = model_params.get("date_features")
      if date_features is None:
          date_features = get_time_features_for_frequency(self.freq)
      self._date_features = date_features
  
      target_transforms = []
      differences = model_params.get("differences")
  
      ts_lengths = train_data.num_timesteps_per_item()
      required_ts_length = sum(differences) + 1
      all_train_ts_are_long_enough = (
          ts_lengths.min() >= required_ts_length
      )
      some_ts_available_for_validation = (
          ts_lengths.max()
          >= required_ts_length + self.prediction_length
      )
      if not (
          all_train_ts_are_long_enough
          and some_ts_available_for_validation
      ):
          logger.warning(
              f"\tTime series in the dataset are too short for chosen differences {differences}. "
              f"Setting differences to [1]."
          )
          differences = [1]
  
      if len(differences) > 0:
          target_transforms.append(Differences(differences))
          self._sum_of_differences = sum(differences)
  
      scaler_name = model_params.get("scaler")
      if scaler_name is None:
          pass
      elif scaler_name == "standard":
          self._scaler = StandardScaler()
      elif scaler_name == "mean_abs":
          self._scaler = MeanAbsScaler()
      else:
          logger.warning(
              f"Unrecognized `scaler` {scaler_name} (supported options: ['standard', 'mean_abs', None]). Scaling disabled."
          )
  
      if self._scaler is not None:
          target_transforms.append(self._scaler)
  
      return {
          "lags": self._target_lags.tolist(),
          "date_features": self._date_features,
          "target_transforms": target_transforms,
      }
  
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 4 for calling the AbstractMLForecastModel obj's underlying member method code with example usage: obj._get_mlforecast_init_args(train_data, model_params) and returns: dict