code: def _get_model_params(self) -> dict:
      model_params = super()._get_model_params().copy()
      model_params.setdefault("max_num_items", 20_000)
      model_params.setdefault("max_num_samples", 1_000_000)
      model_params.setdefault("tabular_hyperparameters", {"GBM": {}})
      model_params.setdefault("tabular_fit_kwargs", {})
      return model_params
  
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 3 for calling the AbstractMLForecastModel obj's underlying member method code with example usage: obj._get_model_params() and returns: model_params