NODE_INSTANCE_GROUP="${NODE_INSTANCE_PREFIX}-group"
fi

if [[ "${KUBERNETES_PROVIDER}" == "gce" ]]; then
  set_num_migs
  NODE_INSTANCE_GROUP=""
  for ((i=1; i<=NUM_MIGS; i++)); do
    if [[ ${i} == "${NUM_MIGS}" ]]; then
      # We are assigning the same mig names as create-nodes function from cluster/gce/util.sh.
      NODE_INSTANCE_GROUP="${NODE_INSTANCE_GROUP}${NODE_INSTANCE_PREFIX}-group"
    else

 * </pre>
 *
 * @author Ben Yu
 * @since 12.0
 */
@ElementTypesAreNonnullByDefault
public abstract class AbstractInvocationHandler implements InvocationHandler {

  private static final Object[] NO_ARGS = {};

  /**
   * {@inheritDoc}
   *
   * <ul>
   *   <li>{@code proxy.hashCode()} delegates to {@link AbstractInvocationHandler#hashCode}

namespace mlir {

namespace {
// This pass converts mlir functions consisting of mlir ops into a tf_executor
// dialect as a single island.
// Result like so:
//   func @my_fn(%argi...) -> (result_t) {
//     %results:[[n_args]] = tf_executor.graph {
//        %island_results:[[nargs + 1]] = tf_executor.island {
//          ... original ops ...
//          tf_executor.yield %results...
//        }
//        tf_executor.fetch %island_results#...

    return it->getFirst().getType();
  };
  auto add_size_vars_to_return = [&](ArrayRef<BlockArgument> new_args) {
    if (new_args.empty()) return;
    auto body_ret = body.front().getTerminator();
    auto new_body_returns = llvm::to_vector<8>(body_ret->getOperands());
    for (auto arg : new_args) new_body_returns.push_back(arg);
    OpBuilder(body_ret).create<func::ReturnOp>(body_ret->getLoc(),

  ValueRange old_args = block.getArguments().take_front(old_args_size);
  ValueRange new_args = block.getArguments().drop_front(old_args_size);
  assert(!new_args.empty());

  ReplaceWithTupleResult(builder, old_args, new_args, /*flatten_tuple=*/true);
  auto new_arg = new_args[new_args.size() - 1];

  block.eraseArguments(0, old_args_size);

  return new_arg;
}

    TF_OpKernelContext* ctx, int index, TF_Status* status);

TF_CAPI_EXPORT extern void TF_LockVariableInfos(TF_VariableInfo** vars,
                                                int num_vars,
                                                TF_Status* status);

TF_CAPI_EXPORT extern void TF_AllocateTempForVariableInfo(
    TF_OpKernelContext* ctx, TF_VariableInfo* var_info, TF_Status* status);

//sys	OpenService(mgr Handle, serviceName *uint16, access uint32) (handle Handle, err error) [failretval==0] = advapi32.OpenServiceW
//sys	DeleteService(service Handle) (err error) = advapi32.DeleteService
//sys	StartService(service Handle, numArgs uint32, argVectors **uint16) (err error) = advapi32.StartServiceW
//sys	QueryServiceStatus(service Handle, status *SERVICE_STATUS) (err error) = advapi32.QueryServiceStatus

	} else {
		psl := state.pendingSlotLocs[:numPieces]
		for i := range psl {
			psl[i] = VarLoc{}
		}
	}
	if cap(state.pendingEntries) < numVars {
		state.pendingEntries = make([]pendingEntry, numVars)
	}
	pe := state.pendingEntries[:numVars]
	freePieceIdx := 0
	for varID, slots := range state.varSlots {
		pe[varID] = pendingEntry{
			pieces: state.pendingSlotLocs[freePieceIdx : freePieceIdx+len(slots)],

# - NUM_WINDOWS_NODES
# exports:
# - NUM_MIGS
# - NUM_WINDOWS_MIGS
function set_num_migs() {
  local defaulted_max_instances_per_mig=${MAX_INSTANCES_PER_MIG:-1000}

  if [[ ${defaulted_max_instances_per_mig} -le "0" ]]; then
    echo "MAX_INSTANCES_PER_MIG cannot be negative. Assuming default 1000"
    defaulted_max_instances_per_mig=1000
  fi

      context.get(), components, device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  int num_axes = TFE_TensorHandleNumDims(combined_value.get(), status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  EXPECT_EQ(num_axes, 1);
}

TEST(PARALLEL_DEVICE, TestNestedParallelDevices) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(