EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  const char* first_device = "/job:worker/replica:0/task:1/device:CPU:0";
  const char* second_device = "/job:worker/replica:0/task:2/device:CPU:0";
  const char* device_name = "/job:localhost/replica:0/task:0/device:CUSTOM:0";
  std::array<const char*, 2> underlying_devices{first_device, second_device};

  // tasks to initialize the eager context and TF server for multi-client
  // execution.
  virtual Status EnableCollectiveOps(const ServerDef& server_def) = 0;

  // Check if the remote task is alive.
  virtual Status CheckRemoteAlive(const std::string& remote_task_name,
                                  bool* is_alive) = 0;

  // Get pointer to the coordination service agent instance.

If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:

1.  Address the perceived conflict directly with those involved, preferably in a
    real-time medium.

  // chosen for the operation by the device placement logic in the
  // executor. After that, the value returned by DeviceName will be a full
  // device name such as "/job:localhost/replica:0/task:0/device:GPU:1".
  virtual const string& DeviceName() const = 0;

  // Sets the operation device name.
  //
  // The given `name` must be parseable by DeviceNameUtils::ParseFullName, and

  // 3. Replace worker1 with a new worker in server_def_0 and server_def_1.
  ReplaceTaskInServerDef(&server_def_0, /*task_index=*/1, "localhost", port);
  ReplaceTaskInServerDef(&server_def_1, /*task_index=*/1, "localhost", port);
  // 4. Start a new task to replace worker1.
  server_def_0.set_task_index(1);
  worker_server1.release();
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

  tensorflow::JobDef* job_def2 = cluster_def->add_job();
  job_def2->set_name("worker");

  // Copy over `host:port` at `task_index`
  for (auto task : cluster_server_def.cluster().job(0).tasks()) {
    if (task.first == task_index) {
      job_def2->mutable_tasks()->insert({task.first, task.second});
    }
  }

  return single_host_server_def;
}

    // During execution:
    //   * task:0 processes main function `VariableAddFunctionWithGraphError`
    //     and places the 'read0_maybe_with_graph_error' op on task:2
    //   * task:0 partitions the main function with a subgraph containing
    //     'read0_maybe_with_graph_error' sent to task:2
    //   * task:2 graph pass reports an error when it sees

  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  const char* device_name = "/job:localhost/replica:0/task:0/device:CUSTOM:0";
  const char* first_device_name =
      "/job:localhost/replica:0/task:0/device:CPU:0";
  const char* second_device_name =
      "/job:localhost/replica:0/task:0/device:CPU:1";
  std::array<const char*, 2> underlying_devices{first_device_name,

// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/job:localhost/replica:0/task:0/device:GPU:1"}
// Then executing an operation on CUSTOM:0 will execute it on GPU:0 and GPU:1.
//

  LoggingDevice* device = new LoggingDevice;
  device->arrived_flag = arrived_flag;
  device->executed_flag = executed_flag;
  device->device_name = name;
  device->underlying_device = "/job:localhost/replica:0/task:0/device:CPU:0";
  device->strict_scope_placement = strict_scope_placement;
  TFE_RegisterCustomDevice(context, custom_device, name, device, status);
}