if tc.resource != nil {
			metrics := &metricsapi.PodMetricsList{}
			for i, resValue := range tc.resource.levels {
				podName := fmt.Sprintf("%s-%d", podNamePrefix, i)
				if len(tc.resource.podNames) > i {
					podName = tc.resource.podNames[i]
				}
				// NB: the list reactor actually does label selector filtering for us,
				// so we have to make sure our results match the label selector
				podMetric := metricsapi.PodMetrics{

    std::vector<const Node*>* body_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  if (num_opers == -1) {
    for (const Node* node : fn_body->graph.op_nodes()) {
      const auto& iter = input_nodes.find(node);
      if (iter == input_nodes.end()) {
        // This node is not referenced in inputs. Add it to the body.
        body_nodes->push_back(node);
      } else {

    # Convolution ouside the while op is quantized.
    self.assertTrue(
        self._contains_op(
            output_graphdef,
            op_name='XlaConvV2',
            attr_name='RhsT',
            attr_val=attr_value_pb2.AttrValue(type=types_pb2.DT_INT8),
        )
    )
    # TODO: b/294783597 - [Converter][TF-Quantizer] Support quantization for the

    const std::unordered_map<string, Node*>& outside_compilation_attr_to_node) {
  std::vector<std::pair<Node*, Node*>>
      lifted_arg_nodes_and_outside_compilation_nodes;
  for (Node* n : function_body.graph->op_nodes()) {
    string oc_cluster;
    if (n->type_string() == "Placeholder" &&
        GetNodeAttr(n->def(), kXlaLiftedArgOutsideCompilationAttrName,
                    &oc_cluster)
            .ok()) {

  TFE_Context* context = TFE_OpGetContext(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  const char* op_name = TFE_OpGetName(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_Op* ret = TFE_NewOp(context, op_name, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  const char* device = TFE_OpGetDevice(other, status);

// can be manually deleted by TF_DeleteOpDefinitionBuilder if it is never
// registered.
TF_CAPI_EXPORT extern TF_OpDefinitionBuilder* TF_NewOpDefinitionBuilder(
    const char* op_name);

// Registers the given op builder with TensorFlow. Indicates success or
// otherwise in the given status.
//
// `builder` is freed whether the op was successfully registered or not. You

func NewPodsIstioProxyImageMismatchInNamespace(r *resource.Instance, podNames []string) diag.Message {
	return diag.NewMessage(
		PodsIstioProxyImageMismatchInNamespace,
		r,
		podNames,
	)
}

// NewConflictingTelemetryWorkloadSelectors returns a new diag.Message based on ConflictingTelemetryWorkloadSelectors.

}
}  // namespace

bool IsSingleGpuGraph(const Graph& g) {
  int gpus_seen = 0;
  absl::flat_hash_set<string> devices_seen;

  for (Node* n : g.op_nodes()) {
    if (devices_seen.contains(n->assigned_device_name())) {
      continue;
    }

    int gpu_number = GetGpuNumber(n->assigned_device_name());
    if (gpu_number != -1) {
      if (++gpus_seen > 1) {

	SampleMeanDivisor func(s []int64) int64      // Function to compute the divisor for mean graphs, or nil
	FormatTag         func(int64, string) string // Function to format a sample tag value into a string
	ObjNames          bool                       // Always preserve obj filename
	OrigFnNames       bool                       // Preserve original (eg mangled) function names

	CallTree     bool // Build a tree instead of a graph

      [&] { TF_CHECK_OK(lib_runtime->ReleaseHandle(handle)); });
  const FunctionBody* fbody = lib_runtime->GetFunctionBody(handle);
  bool is_compilable = true;
  for (const Node* node : fbody->graph->op_nodes()) {
    stack_trace->emplace_back(
        StackFrameView{node->name(), function.name(), node->GetStackTrace()});
    is_compilable &= IsCompilableNode(*node, lib_runtime, stack_trace,