message: [XLA:Python] Use the correct scope for tracemes in local_client.cc.
diff: @@ -134,7 +134,7 @@ static StatusOr<LocalShapedBuffer> TransferHostToDeviceAsync(
  /* static */
  StatusOr<LocalShapedBuffer> LocalShapedBuffer::FromPython(
  const py::object& argument, PyLocalClient* client, int device_ordinal) {
  - tensorflow::profiler::TraceMe("LocalShapedBuffer::FromPython");
  + tensorflow::profiler::TraceMe traceme("LocalShapedBuffer::FromPython");
  TF_ASSIGN_OR_RETURN(PythonBufferTree tree, GetPythonBufferTree(argument));
  // We are done manipulating Python objects; release the GIL.
  @@ -156,7 +156,7 @@ StatusOr<LocalShapedBuffer> LocalShapedBuffer::FromPython(
  LocalShapedBuffer::FromPythonValues(
  const std::vector<std::pair<py::object, int>>& arguments,
  PyLocalClient* client) {
  - tensorflow::profiler::TraceMe("LocalShapedBuffer::FromPythonValues");
  + tensorflow::profiler::TraceMe traceme("LocalShapedBuffer::FromPythonValues");
  int num_arguments = static_cast<int>(arguments.size());
  std::vector<LocalShapedBuffer> outputs(num_arguments);
  if (num_arguments == 0) {
  @@ -240,7 +240,7 @@ const Shape& LocalShapedBuffer::shape() const {
  }
  StatusOr<py::object> LocalShapedBuffer::ToPython() const {
  - tensorflow::profiler::TraceMe("LocalShapedBuffer::ToPython");
  + tensorflow::profiler::TraceMe traceme("LocalShapedBuffer::ToPython");
  auto literal = absl::make_unique<Literal>();
  {
  py::gil_scoped_release gil_release;
  @@ -251,7 +251,7 @@ StatusOr<py::object> LocalShapedBuffer::ToPython() const {
  }
  StatusOr<std::vector<LocalShapedBuffer>> LocalShapedBuffer::DestructureTuple() {
  - tensorflow::profiler::TraceMe("LocalShapedBuffer::DestructureTuple");
  + tensorflow::profiler::TraceMe traceme("LocalShapedBuffer::DestructureTuple");
  const Shape tuple_shape = shape();
  if (!tuple_shape.IsTuple()) {
  @@ -312,7 +312,7 @@ std::vector<int> PyLocalExecutable::DeviceOrdinals() const {
  StatusOr<LocalShapedBuffer> PyLocalExecutable::Execute(
  absl::Span<LocalShapedBuffer* const> argument_handles) {
  - tensorflow::profiler::TraceMe("LocalExecutable::Execute");
  + tensorflow::profiler::TraceMe traceme("LocalExecutable::Execute");
  if (num_replicas() != 1) {
  return InvalidArgument(
  "Attempted to execute computation with %d replicas using Execute()",
  @@ -347,7 +347,7 @@ StatusOr<LocalShapedBuffer> PyLocalExecutable::Execute(
  StatusOr<std::vector<LocalShapedBuffer>> PyLocalExecutable::ExecutePerReplica(
  absl::Span<const std::vector<LocalShapedBuffer*>> argument_handles) {
  - tensorflow::profiler::TraceMe("LocalExecutable::ExecutePerReplica");
  + tensorflow::profiler::TraceMe traceme("LocalExecutable::ExecutePerReplica");
  const int num_devices = client_->device_count();
  if (argument_handles.size() != num_replicas()) {
  @@ -501,7 +501,7 @@ PyLocalExecutable::Compile(const XlaComputation& computation,
  std::vector<Shape> argument_layouts,
  const ExecutableBuildOptions* build_options,
  PyLocalClient* client) {
  - tensorflow::profiler::TraceMe("LocalExecutable::Compile");
  + tensorflow::profiler::TraceMe traceme("LocalExecutable::Compile");
  std::vector<const Shape*> argument_layout_pointers;
  argument_layout_pointers.reserve(argument_layouts.size());
  