class_docstr: Random Forest model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
code: def _get_default_ag_args_ensemble(
      cls, problem_type=None, **kwargs
  ) -> dict:
      default_ag_args_ensemble = super()._get_default_ag_args_ensemble(
          problem_type=problem_type, **kwargs
      )
      if (
          problem_type != QUANTILE
      ):  # use_child_oof not supported in quantile regression
          extra_ag_args_ensemble = {"use_child_oof": True}
          default_ag_args_ensemble.update(extra_ag_args_ensemble)
      return default_ag_args_ensemble
  
imports: import logging
  import math
  import os
  import pickle
  import sys
  import time
  import numpy
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.core.constants import MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from autogluon.core.models import AbstractModel
  from autogluon.core.utils.exceptions import NotEnoughMemoryError, TimeLimitExceeded
  from autogluon.core.utils.utils import normalize_pred_probas
  from autogluon.features.generators import LabelEncoderFeatureGenerator
  from .compilers.native import RFNativeCompiler
  from .compilers.onnx import RFOnnxCompiler
label_desc: Use image node_id 17 for calling the RFModel obj's underlying member method code with example usage: obj._get_default_ag_args_ensemble(cls, problem_type) and returns: default_ag_args_ensemble