class_docstr: Random Forest model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
code: def _estimate_memory_usage(self, X, **kwargs):
      params = self._get_model_params()
      n_estimators_final = params["n_estimators"]
      if isinstance(n_estimators_final, int):
          n_estimators_minimum = min(40, n_estimators_final)
      else:  # if search space
          n_estimators_minimum = 40
      num_trees_per_estimator = self._get_num_trees_per_estimator()
      bytes_per_estimator = (
          num_trees_per_estimator * len(X) / 60000 * 1e6
      )  # Underestimates by 3x on ExtraTrees
      expected_min_memory_usage = (
          bytes_per_estimator * n_estimators_minimum
      )
      return expected_min_memory_usage
  
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 7 for calling the RFModel obj's underlying member method code with example usage: obj._estimate_memory_usage(X) and returns: expected_min_memory_usage