RunMetadata run_metadata_proto;
    result = session->Run(run_options_proto, input_pairs, output_tensor_names,
                          target_oper_names, &outputs, &run_metadata_proto);

    // Serialize back to upstream client, who now owns the new buffer
    if (run_metadata != nullptr) {
      status->status = MessageToBuffer(run_metadata_proto, run_metadata);
      if (!status->status.ok()) return;
    }
  } else {

  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();

                                         size_t length, gcs::Client* gcs_client,
                                         TF_Status* status) {
  std::string bucket, object;
  ParseGCSPath(path, false, &bucket, &object, status);
  if (TF_GetCode(status) != TF_OK)
    return ::testing::AssertionFailure() << TF_Message(status);

  auto writer = gcs_client->WriteObject(bucket, object);
  writer.write(content + offset, length);

              TF_Status* status) {
  google::cloud::StatusOr<gcs::Client> client =
      gcs::Client::CreateDefaultClient();
  if (!client) {
    TF_SetStatusFromGCSStatus(client.status(), status);
    return;
  }

  filesystem->plugin_filesystem =
      new GCSFile(std::move(client.value()), compose, block_size, max_bytes,

  // A single host server def contains a client and the remote host.
  // Example:
  //
  //  Worker1:
  //  cluster { job { name: "client" tasks { key: 0 value: "localhost:14525" } }
  //            job { name: "worker" tasks { key: 1 value: "localhost:14523" } }
  //          } job_name: "worker" task_index: 1 protocol: "grpc"
  //
  //  Worker0:
  //  cluster { job { name: "client" tasks { key: 0 value: "localhost:14526" } }

  TFE_Context* ctx = TFE_NewContext(opts, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  server_def.set_task_index(0);
  auto cluster = server_def.mutable_cluster();
  auto client_job = cluster->add_job();
  client_job->set_name("localhost");
  int client_port = tensorflow::testing::PickUnusedPortOrDie();
  client_job->mutable_tasks()->insert(

      static_cast<tensorflow::ContextDevicePlacementPolicy>(policy));
}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.
extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy(
    TFE_Context* ctx) {