How to use the alibi.explainers.anchor_base.AnchorBaseBeam.dlow_bernoulli function in alibi

best_of_size[current_size] = [tuples[x] for x in chosen_tuples]
            if verbose:
                print('Best of size ', current_size, ':')

            # for each candidate anchor:
            # update precision, lower and upper bounds until precision constraints are met
            # update best anchor if coverage is larger than current best coverage
            stop_this = False
            for i, t in zip(chosen_tuples, best_of_size[current_size]):

                # choose at most (beam_size - 1) tuples at each step with at most n_feature steps
                beta = np.log(1. / (delta / (1 + (beam_size - 1) * n_features)))

                # get precision, lower and upper bounds, and coverage for candidate anchor
                mean = state['t_positives'][t] / state['t_nsamples'][t]
                lb = AnchorBaseBeam.dlow_bernoulli(mean, beta / state['t_nsamples'][t])
                ub = AnchorBaseBeam.dup_bernoulli(mean, beta / state['t_nsamples'][t])
                coverage = state['t_coverage'][t]

                if verbose:
                    print(i, mean, lb, ub)

                # while prec(A) >= tau and prec_lb(A) < tau - eps or prec(A) < tau and prec_ub(A) > tau + eps
                # sample more data and update lower and upper precision bounds ...
                # ... b/c respectively either prec_lb(A) or prec(A) needs to improve
                while ((mean >= desired_confidence and lb < desired_confidence - epsilon_stop) or
                       (mean < desired_confidence and ub >= desired_confidence + epsilon_stop)):
                    # sample a batch of data, get new precision, lb and ub values
                    sample_fns[i](batch_size)
                    mean = state['t_positives'][t] / state['t_nsamples'][t]
                    lb = AnchorBaseBeam.dlow_bernoulli(mean, beta / state['t_nsamples'][t])
                    ub = AnchorBaseBeam.dup_bernoulli(mean, beta / state['t_nsamples'][t])