std::string FindFilePrefixTensorName(const GraphDef& graph_def) {
  for (const NodeDef& node_def : graph_def.node()) {
    if (node_def.op() == FunctionLibraryDefinition::kArgOp) {
      // Matches the `tf_saved_model.index_path = ["__tf_file_prefix"]`.
      const auto index_path_attr_itr =
          node_def.attr().find(kTfSavedModelIndexPathAttr.str());
      if (index_path_attr_itr != node_def.attr().end()) {

                               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;

      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);

namespace tensorflow {

std::shared_ptr<NodeProperties> ToNodeProperties(const string& text) {
  NodeDef node_def;
  DataTypeVector dummy;
  EXPECT_TRUE(protobuf::TextFormat::MergeFromString(text, &node_def));
  return std::make_shared<NodeProperties>(nullptr, std::move(node_def), dummy,
                                          dummy);
}

// Create a FunctionDef that takes one resource and one regular param

  return CanCreateXlaKernel(props->node_def) &&
         !XlaOpRegistry::IsCompilationDevice(flr.device()->device_type());
}

static Status CreateXlaKernel(FunctionLibraryRuntime* flr,
                              const NodeDef& node_def,
                              std::unique_ptr<OpKernel>* kernel) {
  if (!CanCreateXlaKernel(node_def)) {
    return errors::Internal("Invalid node: ", node_def.ShortDebugString());
  }

  // 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>

    NodeDef node_def;
    node_def.ParseFromArray(node_def_buf->data, node_def_buf->length);
    EXPECT_EQ(node_def.op(), "TestKernelAttrGetNodeDef");
    EXPECT_EQ(node_def.name(), "FakeNode");
    EXPECT_EQ(node_def.device(), "FakeDevice");
    EXPECT_EQ(node_def.attr_size(), 1);
    const ::tensorflow::AttrValue& value = node_def.attr().at("Attr");

constexpr const char* kPjRtDeviceCompilationProfilerResourceName =
    "pjrt_device_compilation_profiler";
}  // namespace

absl::StatusOr<std::unique_ptr<Graph>> CreateSingleOpGraph(
    const NodeDef& node_def, absl::Span<const XlaArgument> args,
    absl::Span<const DataType> result_types) {
  // TODO(b/74182462): We implement this by creating a new dummy Graph including

                                     old_node->out_edges().end());
  for (const Edge* edge : out_edges) {
    // TODO(sanjoy): This does not update NodeDef inputs.  To be able to update
    // NodeDef inputs we first need to fix encapsulate_subgraphs_pass to fix up
    // the NodeDef inputs to the function call nodes.
    g->AddEdge(new_node, edge->src_output(), edge->dst(), edge->dst_input());
    g->RemoveEdge(edge);
  }
}

}

const SavedObjectGraph& TFPackage::GetObjectGraph() {
  return saved_model_proto_.mutable_meta_graphs(0)->object_graph_def();
}

tensorflow::StatusOr<const tensorflow::NodeDef*> TFPackage::GetGraphDefNode(
    std::string name) {
  const auto& iter = graph_def_nodes_by_name_.find(name);
  if (iter == graph_def_nodes_by_name_.end()) {
    return tensorflow::Status(absl::StatusCode::kInternal,