class_docstr: Wraps an estimator to use :func:`concordance_index_ipcw` as ``score`` function.
  
  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.
  
  tau : float, optional
      Truncation time. The survival function for the underlying
      censoring time distribution :math:`D` needs to be positive
      at `tau`, i.e., `tau` should be chosen such that the
      probability of being censored after time `tau` is non-zero:
      :math:`P(D > \tau) > 0`. If `None`, no truncation is performed.
  
  tied_tol : float, optional, default: 1e-8
      The tolerance value for considering ties.
      If the absolute difference between risk scores is smaller
      or equal than `tied_tol`, risk scores are considered tied.
  
  Attributes
  ----------
  estimator_ : estimator
      Estimator that was fit.
  
  See also
  --------
  concordance_index_ipcw
code: def __init__(self, estimator, tau=None, tied_tol=1e-8):
      super().__init__(
          estimator=estimator,
          predict_func="predict",
          score_func=concordance_index_ipcw,
          score_index=0,
          greater_is_better=True,
      )
      self.tau = tau
      self.tied_tol = tied_tol
  
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 1 to create a new as_concordance_index_ipcw_scorer object from inherited base classes: _ScoreOverrideMixin, BaseEstimator with example: obj = as_concordance_index_ipcw_scorer(estimator, tau, tied_tol)