string serialized_server_def_0 = server_def_0.SerializeAsString();
  string serialized_server_def_1 = server_def_1.SerializeAsString();

  // Create two worker tasks.
  server_def_0.set_task_index(1);
  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def_0, tensorflow::Env::Default(), &worker_server1)
                  .ok());

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  auto* arg1 = TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Create a first "Add" computing `arg0 + arg1`.
  TF_AbstractTensor* add_output1;
  {
    // Build an abstract operation, inputs and output.
    auto* add_op = TF_NewAbstractOp(graph_ctx);

                                               TransactionToken* token) {
  if (ops_->recursively_create_dir == nullptr)
    return FileSystem::RecursivelyCreateDir(dirname, token);

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  std::string translated_name = TranslateName(dirname);
  ops_->recursively_create_dir(filesystem_.get(), translated_name.c_str(),
                               plugin_status.get());

    calls++;
    memset(buffer, 'x', n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  std::vector<char> out;
  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  // Create a cache with max staleness of 2 seconds, and verify that it works as
  // expected.
  tf_gcs_filesystem::RamFileBlockCache cache1(
      8, 16, 2 /* max staleness */, fetcher,
      [&env]() { return env->NowSeconds(); });

  // reinserted into the cache in UpdateLRU.
  entry->second->timestamp = 0;
  lru_list_.erase(entry->second->lru_iterator);
  lra_list_.erase(entry->second->lra_iterator);
  cache_size_ -= entry->second->data.capacity();
  block_map_.erase(entry);
}

//      TP_OPTIMIZER_CONFIGS_STRUCT_SIZE; params->optimizer_configs->remapping =
//      TF_TriState_Off;
//
//      // Set functions to create a new optimizer.
//      params->optimizer->struct_size = TP_OPTIMIZER_STRUCT_SIZE;
//      params->optimizer->create_func = (My_optimizer::create_func);
//    }

#define GO_MAJOR 0
#define GO_MINOR 0
#define GO_PATCH 1

#ifdef __cplusplus
extern "C" {
#endif

  RegisterLoggingDevice(context.get(), name, /*strict_scope_placement=*/true,
                        &arrived, &executed, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  // Create a variable handle placed on the custom device.
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context.get(), "VarHandleOp", status.get()), TFE_DeleteOp);

    test to timeout but it could be useful when you need to execute a single
    test or more in general your filtered/selected tests have a very low
    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash

// The context should be identical to that of the Tensor.
TF_CAPI_EXPORT TFE_TensorHandle* TFE_NewTensorHandleFromTensor(
    TFE_Context* ctx, TF_Tensor* t, TF_Status* status);

// Create a packed TensorHandle with the given list of TensorHandles.
// If `handles` are on the same device, assign the same device to the packed
// handle; if `handles` are on different deivces, assign a CompositeDevice to
// it.

// TF_Tensor).
TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,
                                                         TF_Status* status);

// Create a new TFE_TensorHandle with the same contents as 'h' but placed
// in the memory of the device name 'device_name'.
// If source and destination are the same device, then this creates a new handle