class_docstr: Random Forest model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
code: def _more_tags(self):
      # `can_refit_full=True` because final n_estimators is communicated at end of `_fit`:
      #  `self.params_trained['n_estimators'] = self.model.n_estimators`
      tags = {"can_refit_full": True}
      if self.problem_type == QUANTILE:
          tags[
              "valid_oof"
          ] = False  # not supported in quantile regression
      else:
          tags["valid_oof"] = True
      return tags
  
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 18 for calling the RFModel obj's underlying member method code with example usage: obj._more_tags() and returns: tags