func (sys *IAMSys) loadWatchedEvent(ctx context.Context, event iamWatchEvent) (err error) {
	usersPrefix := strings.HasPrefix(event.keyPath, iamConfigUsersPrefix)
	groupsPrefix := strings.HasPrefix(event.keyPath, iamConfigGroupsPrefix)
	stsPrefix := strings.HasPrefix(event.keyPath, iamConfigSTSPrefix)
	svcPrefix := strings.HasPrefix(event.keyPath, iamConfigServiceAccountsPrefix)
	policyPrefix := strings.HasPrefix(event.keyPath, iamConfigPoliciesPrefix)

			},
		})
	}
	lc := Lifecycle{
		Rules: rules,
	}
	if event := lc.NoncurrentVersionsExpirationLimit(ObjectOpts{Name: "obj"}); event.RuleID != "1" || event.NoncurrentDays != 1 || event.NewerNoncurrentVersions != 1 {
		t.Fatalf("Expected (ruleID, days, lim) to be (\"1\", 1, 1) but got (%s, %d, %d)", event.RuleID, event.NoncurrentDays, event.NewerNoncurrentVersions)
	}
}

	fi.TransitionedObjName = destObj
	fi.TransitionTier = opts.Transition.Tier
	fi.TransitionVersionID = string(rv)
	eventName := event.ObjectTransitionComplete

	storageDisks := er.getDisks()

	if err = er.deleteObjectVersion(ctx, bucket, object, fi, false); err != nil {
		eventName = event.ObjectTransitionFailed
	}

	for _, disk := range storageDisks {
		if disk != nil && disk.IsOnline() {
			continue

// that defines two inputs, "concat_dim" and "values" (in that order).
// You must use TF_AddInput() for the first input (since it takes a
// single tensor), and TF_AddInputList() for the second input (since
// it takes a list, even if you were to pass a list with a single
// tensor), as in:
//   TF_OperationDescription* desc = TF_NewOperation(graph, "Concat", "c");
//   TF_Output concat_dim_input = {...};
//   TF_AddInput(desc, concat_dim_input);

// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
//
// You should have received a copy of the GNU Affero General Public License

        ModelBuildingListener listener = request.getModelBuildingListener();

        if (listener != null) {
            ModelBuildingEvent event = new DefaultModelBuildingEvent(model, request, problems);

            catapult.fire(listener, event);
        }
    }

    private boolean containsCoordinates(String message, String groupId, String artifactId, String version) {
        return message != null

                            project, convert(r.getProblemCollector()), resolutionResult));
                } else {
                    // Extract project identification even when effective model is null
                    String projectId = extractProjectId(r);
                    File sourcePomFile = r.getSource() != null && r.getSource().getPath() != null

config_setting(
    name = "with_xla_support",
    define_values = {"with_xla_support": "true"},
    visibility = ["//visibility:public"],
)

# By default, XLA GPU is compiled into tensorflow when building with
# --config=cuda even when `with_xla_support` is false. The config setting
# here allows us to override the behavior if needed.
config_setting(
    name = "no_xla_deps_in_cuda",
    define_values = {"no_xla_deps_in_cuda": "true"},

	enc := json.NewEncoder(buf)
	tmpEvt := struct{ Records []event.Event }{[]event.Event{{}}}
	for {
		select {
		case <-ctx.Done():
			grid.PutByteBuffer(buf.Bytes())
			return nil
		case ev := <-ch:
			buf.Reset()
			tmpEvt.Records[0] = ev
			if err := enc.Encode(tmpEvt); err != nil {
				peersLogOnceIf(ctx, err, "event: Encode failed")
				continue
			}

		apiErr = ErrFilterNameInvalid
	case *event.ErrFilterNamePrefix:
		apiErr = ErrFilterNamePrefix
	case *event.ErrFilterNameSuffix:
		apiErr = ErrFilterNameSuffix
	case *event.ErrInvalidFilterValue:
		apiErr = ErrFilterValueInvalid
	case *event.ErrDuplicateEventName:
		apiErr = ErrOverlappingConfigs
	case *event.ErrDuplicateQueueConfiguration:
		apiErr = ErrOverlappingFilterNotification