src->name());
      placeholder_builder.Attr(kOutsideCompilationSrcOutputAttrName,
                               src_output);
      NodeDef placeholder_def;
      TF_RETURN_IF_ERROR(placeholder_builder.Finalize(&placeholder_def));
      TF_ASSIGN_OR_RETURN(placeholder_node, g->AddNode(placeholder_def));
      placeholders[placeholder_index] = placeholder_node;

	im := &realImageGCManager{
		runtime:       runtime,
		policy:        policy,
		imageRecords:  make(map[string]*imageRecord),
		statsProvider: statsProvider,
		recorder:      recorder,
		nodeRef:       nodeRef,
		tracer:        tracer,
	}

	return im, nil
}

func (im *realImageGCManager) Start() {
	ctx := context.Background()
	go wait.Until(func() {
		_, err := im.detectImages(ctx, time.Now())

	nodeConditions []v1.NodeConditionType
	// captures when a node condition was last observed based on a threshold being met
	nodeConditionsLastObservedAt nodeConditionsObservedAt
	// nodeRef is a reference to the node
	nodeRef *v1.ObjectReference
	// used to record events about the node
	recorder record.EventRecorder
	// used to measure usage stats on system
	summaryProvider stats.SummaryProvider

      TF_NewGraphProperties(reinterpret_cast<TF_GrapplerItem*>(&item));
  TF_InferStatically(graph_properties, true, false, false, false, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  for (const NodeDef& node : item.graph.node()) {
    if (node.op() == "AddN") {
      int num_values = 0;
      TF_GetInputPropertiesListSize(graph_properties, node.name().c_str(),
                                    &num_values, status);

	}
	manager := &managerImpl{
		logger:                           conf.Logger,
		probeManager:                     conf.ProbeManager,
		recorder:                         conf.Recorder,
		nodeRef:                          conf.NodeRef,
		getPods:                          conf.GetPodsFunc,
		killPodFunc:                      conf.KillPodFunc,
		syncNodeStatus:                   conf.SyncNodeStatusFunc,

  // CHECK: %0 = mhlo.constant dense<[32, 12, 12, 64]> : tensor<4xi32>
  // CHECK-NEXT: %1 = "tf.RandomUniform"(%0) : (tensor<4xi32>) -> tensor<32x12x12x64xf32>
  // expected-remark@+1 {{failed to create tf2xla kernel: INVALID_ARGUMENT: NodeDef missing attrs 'seed2', 'seed' from}}
  %cst = "tf.Const"() {value = dense<[32, 12, 12, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
  %0 = "tf.RandomUniform"(%cst) {} : (tensor<4xi32>) -> tensor<32x12x12x64xf32>

// Belows are the attributes in TFE.
// TFE Arguments and Results (Got from "_Arg",
// "_Retval", .etc)
//  NodeDef.device <-> "tf.device"
//  NodeDef.attr <-> "tf."
//
// TFE general operations
//  NodeDef.device <-> "device"
//
// The following two functions are only used for mapping/excluding attributes
// which are inconsistent between TFG and TFE.
//

  TF_ASSERT_OK(DeviceFactory::AddDevices(
      SessionOptions(), "/job:localhost/replica:0/task:0", &devices));

  // Scope::ToGraph loses the assigned device name since it goes through
  // GraphDef/NodeDef which does not have a field for the assigned device name.
  Node* n = FindNodeByName(*graph, "shape");
  ASSERT_NE(n, nullptr);
  n->set_assigned_device_name(
      "/job:localhost/replica:0/task:0/device:XLA_GPU:0");

    // In both cases, none should be variables.
    size_t expected_nodes = use_resource ? (use_identity ? 5 : 4) : 3;

    EXPECT_EQ(frozen_graph_def.node_size(), expected_nodes);
    for (const NodeDef& node : frozen_graph_def.node()) {
      EXPECT_NE(node.op(), "Variable") << node.name();
      EXPECT_NE(node.op(), "VariableV2") << node.name();
      EXPECT_NE(node.op(), "VarHandleOp") << node.name();

  for i in "${!all_selected_nodes[@]}"; do
    node_name="${all_selected_nodes[$i]}"
    node_dir="${report_dir}/${node_name}"
    mkdir -p "${node_dir}"
    if [[ "${i}" -lt "${#linux_nodes_selected_for_logs[@]}" ]]; then
      # Save logs in the background. This speeds up things when there are
      # many nodes.
      save-logs "${node_name}" "${node_dir}" "${node_logfiles_all}" "${node_systemd_services}" &
    else