bool UsesFunctionLibrary() const override { return false; }
  Status Init(
      const tensorflow::RewriterConfig_CustomGraphOptimizer* config) override {
    return OkStatus();
  }
  Status Optimize(Cluster* cluster, const GrapplerItem& item,
                  GraphDef* optimized_graph_def) override;

  ~CGraphOptimizer() override {
    if (optimizer_.destroy_func != nullptr) {
      (*optimizer_.destroy_func)(c_optimizer_);

    const tensorflow::ServerDef& cluster_server_def, int task_index) {
  tensorflow::ServerDef single_host_server_def;
  single_host_server_def.set_job_name("worker");
  single_host_server_def.set_protocol(cluster_server_def.protocol());
  single_host_server_def.set_task_index(0);
  tensorflow::ClusterDef* cluster_def =
      single_host_server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->add_job();

  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(

 public:
  virtual ~ImmediateExecutionDistributedManager() {}

  // Set up distributed execution environment on local and remote tasks.
  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after

  tensorflow::ServerDef server_def;
  server_def.set_protocol("grpc");
  server_def.set_job_name(job_name);
  server_def.set_task_index(0);
  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->add_job();
  job_def->set_name(job_name);
  for (int i = 0; i < num_tasks; i++) {
    int port = tensorflow::testing::PickUnusedPortOrDie();
    job_def->mutable_tasks()->insert(

  TF_DeleteStatus(status);
  TF_ASSERT_OK(cluster->Shutdown());
}

TEST(TF_GraphProperties, OutputProperties) {
  std::unique_ptr<SingleMachine> cluster(new SingleMachine(5 * 60, 3, 0));
  TF_ASSERT_OK(cluster->Provision());

  TrivialTestGraphInputYielder fake_input(4, 1, 10, false,
                                          cluster->GetDeviceNames());
  GrapplerItem item;

#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/cluster.pb.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

namespace {

using ::tensorflow::string;

// A Server instance encapsulates a set of devices and a Session target that
// can participate in distributed training. A server belongs to a cluster
// (specified by a ClusterSpec), and corresponds to a particular task in a
// named job. The server can communicate with any other server in the same
// cluster.

// In-process TensorFlow server.
typedef struct TF_Server TF_Server;

  CheckRemoteMatMulExecutesOK(ctx, remote_device_name, local_device_name);

  // Adding a non-existent remote worker to cluster def. This should cause the
  // UpdateServerDef call to fail.
  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->mutable_job(0);
  int port = tensorflow::testing::PickUnusedPortOrDie();
  job_def->mutable_tasks()->insert(

system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas...