class_docstr: Wraps an estimator to use the negative of :func:`integrated_brier_score` as ``score`` function.
  
  The estimator needs to be able to estimate survival functions via
  a ``predict_survival_function`` method.
  
  See the :ref:`User Guide </user_guide/evaluating-survival-models.ipynb#Using-Metrics-in-Hyper-parameter-Search>`
  for using it for hyper-parameter optimization.
  
  Parameters
  ----------
  estimator : object
      Instance of an estimator that provides ``predict_survival_function``.
  
  times : array-like, shape = (n_times,)
      The time points for which to estimate the Brier score.
      Values must be within the range of follow-up times of
      the test data `survival_test`.
  
  Attributes
  ----------
  estimator_ : estimator
      Estimator that was fit.
  
  See also
  --------
  integrated_brier_score
code: def _do_predict(self, X):
      predict_func = getattr(self.estimator_, self._predict_func)
      surv_fns = predict_func(X)
      times = self.times
      estimates = np.empty((len(surv_fns), len(times)))
      for i, fn in enumerate(surv_fns):
          estimates[i, :] = fn(times)
      return estimates
  
imports: import numpy
  from sklearn.base import BaseEstimator
  from sklearn.utils import check_array, check_consistent_length
  from sklearn.utils.metaestimators import available_if
  from sklearn.utils.validation import check_is_fitted
  from .exceptions import NoComparablePairException
  from .nonparametric import CensoringDistributionEstimator, SurvivalFunctionEstimator
  from .util import check_y_survival
label_desc: Use image node_id 2 for calling the as_integrated_brier_score_scorer obj's underlying member method code with example usage: obj._do_predict(X) and returns: estimates