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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def concordance_index_ipcw(survival_train, survival_test, estimate, tau=None, tied_tol=1e-8):
 """Concordance index for right-censored data based on inverse probability of censoring weights. This is an alternative to the estimator in :func:`concordance_index_censored` that does not depend on the distribution of censoring times in the test data. Therefore, the estimate is unbiased and consistent for a population concordance measure that is free of censoring. It is based on inverse probability of censoring weights, thus requires access to survival times from the training data to estimate the censoring distribution. Note that this requires that survival times `survival_test` lie within the range of survival times `survival_train`. This can be achieved by specifying the truncation time `tau`. The resulting `cindex` tells how well the given prediction model works in predicting events that occur in the time range from 0 to `tau`. The estimator uses the Kaplan-Meier estimator to estimate the censoring survivor function. Therefore, it is restricted to situations where the random censoring assumption holds and censoring is independent of the features. Parameters survival_train : structured array, shape = (n_train_samples,) Survival times for training data to estimate the censoring distribution from. A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field. survival_test : structured array, shape = (n_samples,) Survival times of test data. A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field. estimate : array-like, shape = (n_samples,) Estimated risk of experiencing an event of test data. 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. 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 References .. [1] Uno, H., Cai, T., Pencina, M. J., D’Agostino, R. B., & Wei, L. J. (2011). "On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data". Statistics in Medicine, 30(10), 1105–1117. """

    test_event, test_time = check_y_survival(survival_test)

    if tau is not None:
        survival_test = survival_test[test_time < tau]

    estimate = check_array(estimate, ensure_2d=False)
    check_consistent_length(test_event, test_time, estimate)

    cens = CensoringDistributionEstimator()
    cens.fit(survival_train)
    ipcw = cens.predict_ipcw(survival_test)

    w = numpy.square(ipcw)

    return _estimate_concordance_index(test_event, test_time, estimate, w, tied_tol)