// ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {

  bool downloaded_block = false;
  auto reconcile_state = MakeCleanup([this, &downloaded_block, &key, &block] {
    // Perform this action in a cleanup callback to avoid locking mu_ after
    // locking block->mu.
    if (downloaded_block) {
      absl::MutexLock l(&mu_);
      // Do not update state if the block is already to be evicted.
      if (block->timestamp != 0) {

    // .backing_device of shape is CPU since the tensor is backed by CPU
    backing_device_name =
        TFE_TensorHandleBackingDeviceName(retvals[0], status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
    ASSERT_TRUE(absl::StrContains(backing_device_name, "CPU:0"))
        << backing_device_name;

    TFE_DeleteOp(shape_op);

#ifndef TENSORFLOW_C_EAGER_TRACING_UTILS_H_
#define TENSORFLOW_C_EAGER_TRACING_UTILS_H_

#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {
namespace tracing {
Status MaybeSetOpName(AbstractOperation*, const char* op_name);
}  // namespace tracing
}  // namespace tensorflow

#include "tensorflow/c/eager/tracing_utils.h"

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace tracing {

Status MaybeSetOpName(AbstractOperation* op, const char* op_name) {

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python
TFCI_DOCKER_PULL_ENABLE=1

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Release jobs are very basic. They don't use any caching or RBE,
# but they do upload logs to resultstore.
# IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to
# pretend the path is a directory.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1

      TF_DeleteTensor);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_EQ(111., *static_cast<float*>(TF_TensorData(resolved_value.get())));

  // Free the backing buffer for the variable.
  op.reset(TFE_NewOp(context.get(), "DestroyResourceOp", status.get()));
  TFE_OpAddInput(op.get(), var_handle, status.get());
  TFE_OpSetDevice(op.get(), name, status.get());

#   public URL to share your build results with collaborators. However,
#   it is only available to a limited set of internal TensorFlow developers.
#
#   RBE is incompatible with local caching, so you must remove
#   disk_cache, public_cache, and public_cache_push from your $TFCI file.
#
# To use RBE, you must first run `gcloud auth application-default login`, then:
export TFCI=py311,linux_x86,rbe

    ASSERT_TRUE(remote_arg->HasLocalMirror(nullptr));
  }

  if (remote_func_outputs) {
    const string backing_device =
        TFE_TensorHandleBackingDeviceName(retvals[0], status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    EXPECT_EQ(backing_device, task2_name);
  }

  auto* retval_task0 = TFE_TensorHandleCopyToDevice(