Custom operation ID to be used by this *path operation*.

                By default, it is generated automatically.

                If you provide a custom operation ID, you need to make sure it is
                unique for the whole API.

                You can customize the
                operation ID generation with the parameter

Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.

  }
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  // TODO(apassos) do we need to wrap the deallocator here to make sure to sync
  // the device?
  TF_ManagedBuffer* buf =
      new TF_ManagedBuffer(data, len, deallocator, deallocator_arg,
                           /*owns_memory=*/false);

  Run({{func_feed, Int32Tensor(2)}}, func_op, 2 /*+=*/ + 5 + 1);

  // Verify input, output, and subset of edges in fdef.
  // The subset of edges we verify is a chain between feed1 and output to
  // make sure that the correct output is picked.
  tensorflow::FunctionDef fdef;
  ASSERT_TRUE(GetFunctionDef(func_, &fdef));
  VerifyFDefInputs(fdef, M({{"feed1"}, {"feed2"}}));
  VerifyFDefOutputs(fdef, M({{"test_loop_exit"}}));

	if err == errConfigNotFound {
		err = errNoSuchGroup
	}
	return err
}

func (ies *IAMEtcdStore) watch(ctx context.Context, keyPath string) <-chan iamWatchEvent {
	ch := make(chan iamWatchEvent)

	// go routine to read events from the etcd watch channel and send them
	// down `ch`
	go func() {
		for {
		outerLoop:
			watchCh := ies.client.Watch(ctx,

	if err != nil {
		return nil, err
	}
	defer conn.Close()

	// Bind to the lookup user account
	if err = l.LDAP.LookupBind(conn); err != nil {
		return nil, err
	}

	res := make(map[string]set.StringSet, len(userDistNames))
	for _, userDistName := range userDistNames {
		username := userDNToUsernameMap[userDistName]
		groups, err := l.LDAP.SearchForUserGroups(conn, username, userDistName)

	case sqlFnDateDiff:
		return handleDateDiff(r, e.DateDiff, tableAlias)

	}

	// For all simple argument functions, we evaluate the arguments here
	argVals := make([]*Value, len(e.SFunc.ArgsList))
	for i, arg := range e.SFunc.ArgsList {
		argVals[i], err = arg.evalNode(r, tableAlias)
		if err != nil {
			return nil, err
		}
	}

	switch e.getFunctionName() {

      TFE_TensorHandleDevicePointer(tensor, status));
  if (TF_GetCode(status) != TF_OK) return nullptr;
  if (parallel_tensor->num_tensors() == 1) {
    // Copy-off for single-device tensors is allowed to make debugging dynamic
    // control flow easier.
    return TFE_TensorHandleCopySharingTensor(parallel_tensor->tensor(0),
                                             status);
  } else {
    TF_SetStatus(

    ImmutableSet.Builder<ImmutableList<Class<?>>> allOverloads = ImmutableSet.builder();
    // The first two are for the overloads that don't take formatting args, e.g.
    // checkArgument(boolean) and checkArgument(boolean, Object)
    allOverloads.add(ImmutableList.<Class<?>>of(predicateType));
    allOverloads.add(ImmutableList.<Class<?>>of(predicateType, Object.class));

 *
 * <ul>
 *   <li>Test cases related to whether the graph is directed or undirected.
 *   <li>Test cases related to the specific implementation of the {@link Graph} interface.
 * </ul>
 *
 * TODO(user): Make this class generic (using <N, E>) for all node and edge types.
 * TODO(user): Differentiate between directed and undirected edge strings.
 */
public abstract class AbstractGraphTest {

  Graph<Integer> graph;

  /**