code: def concordance_index_censored(
      event_indicator, event_time, estimate, tied_tol=1e-8
  ):
      """Concordance index for right-censored data
  
      The concordance index is defined as the proportion of all comparable pairs
      in which the predictions and outcomes are concordant.
  
      Two samples are comparable if (i) both of them experienced an event (at different times),
      or (ii) the one with a shorter observed survival time experienced an event, in which case
      the event-free subject "outlived" the other. A pair is not comparable if they experienced
      events at the same time.
  
      Concordance intuitively means that two samples were ordered correctly by the model.
      More specifically, two samples are concordant, if the one with a higher estimated
      risk score has a shorter actual survival time.
      When predicted risks are identical for a pair, 0.5 rather than 1 is added to the count
      of concordant pairs.
  
      See the :ref:`User Guide </user_guide/evaluating-survival-models.ipynb>`
      and [1]_ for further description.
  
      Parameters
      ----------
      event_indicator : array-like, shape = (n_samples,)
          Boolean array denotes whether an event occurred
  
      event_time : array-like, shape = (n_samples,)
          Array containing the time of an event or time of censoring
  
      estimate : array-like, shape = (n_samples,)
          Estimated risk of experiencing an event
  
      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.
  
      Returns
      -------
      cindex : float
          Concordance index
  
      concordant : int
          Number of concordant pairs
  
      discordant : int
          Number of discordant pairs
  
      tied_risk : int
          Number of pairs having tied estimated risks
  
      tied_time : int
          Number of comparable pairs sharing the same time
  
      See also
      --------
      concordance_index_ipcw
          Alternative estimator of the concordance index with less bias.
  
      References
      ----------
      .. [1] Harrell, F.E., Califf, R.M., Pryor, D.B., Lee, K.L., Rosati, R.A,
             "Multivariable prognostic models: issues in developing models,
             evaluating assumptions and adequacy, and measuring and reducing errors",
             Statistics in Medicine, 15(4), 361-87, 1996.
      """
      event_indicator, event_time, estimate = _check_inputs(
          event_indicator, event_time, estimate
      )
  
      w = np.ones_like(estimate)
  
      return _estimate_concordance_index(
          event_indicator, event_time, estimate, w, 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 7 for calling a global function with example usage: concordance_index_censored(event_indicator, event_time, estimate, tied_tol) and returns: _estimate_concordance_index