class_docstr: Abstract base class for Gradient Boosting.
class_docstr_tok: ["Abstract","base","class","for","Gradient","Boosting","."]
docstr: Check that the estimator is initialized, raising an error if not.
code: def _check_initialized(self):
      """Check that the estimator is initialized, raising an error if not."""
      check_is_fitted(self)
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  from time import time
  import numpy
  from scipy.sparse import csc_matrix, csr_matrix, issparse
  from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ..exceptions import NotFittedError
  from ..model_selection import train_test_split
  from ..tree import DecisionTreeRegressor
  from ..tree._tree import DOUBLE, DTYPE
  from ..utils import check_array, check_random_state, column_or_1d
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _check_sample_weight, check_is_fitted
  from .None import _gb_losses
  from ._base import BaseEnsemble
  from ._gradient_boosting import _random_sample_mask, predict_stage, predict_stages
label_desc: Use image node_id 9 for calling the BaseGradientBoosting obj's underlying member method code with example usage: obj._check_initialized() without return types