class_docstr: XGBoost model: https://xgboost.readthedocs.io/en/latest/
  
  Hyperparameter options: https://xgboost.readthedocs.io/en/latest/parameter.html
class_docstr_tok: ["XGBoost","model",":","https",":","\/\/xgboost.readthedocs.io\/en\/latest\/","Hyperparameter","options",":","https",":","\/\/xgboost.readthedocs.io\/en\/latest\/parameter.html"]
code: def _predict_proba(self, X, num_cpus=-1, **kwargs):
      X = self.preprocess(X, **kwargs)
      if self.problem_type in [MULTICLASS, SOFTCLASS]:
          # Bug fix for "xgboost>=2,<2.0.3" : https://github.com/dmlc/xgboost/issues/9807
          self.model.set_params(
              n_jobs=num_cpus, objective="multi:softprob"
          )
      else:
          self.model.set_params(n_jobs=num_cpus)
  
      if self.problem_type == REGRESSION:
          return self.model.predict(X)
  
      y_pred_proba = self.model.predict_proba(X)
      return self._convert_proba_to_unified_form(y_pred_proba)
  
imports: import logging
  import math
  import os
  import time
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT
  from autogluon.common.utils.lite import disable_if_lite_mode
  from autogluon.common.utils.pandas_utils import get_approximate_df_mem_usage
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.common.utils.try_import import try_import_xgboost
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, REGRESSION, SOFTCLASS
  from autogluon.core.models import AbstractModel
  from autogluon.core.models._utils import get_early_stopping_rounds
  from .None import xgboost_utils
  from .hyperparameters.parameters import get_param_baseline
  from .hyperparameters.searchspaces import get_default_searchspace
label_desc: Use image node_id 9 for calling the XGBoostModel obj's underlying member method code with example usage: obj._predict_proba(X, num_cpus) and returns: self, self