void TF_DeleteAbstractFunction(TF_AbstractFunction* func) {
  unwrap(func)->Unref();
}

void TF_ExecutionContextRegisterFunction(TF_ExecutionContext* ctx,
                                         TF_AbstractFunction* func,
                                         TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s,
                                 unwrap(ctx)->RegisterFunction(unwrap(func)));

}

void TF_SetAttrFuncName(TF_OperationDescription* desc, const char* attr_name,
                        const char* value, size_t length) {
  tensorflow::NameAttrList func_name;
  func_name.set_name(string(value, value + length));
  desc->node_builder.Attr(attr_name, func_name);
}

void TF_SetAttrShape(TF_OperationDescription* desc, const char* attr_name,
                     const int64_t* dims, int num_dims) {

namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),
      func_record_(new FunctionRecord(std::move(fdef), {}, true)) {}
GraphFunction::~GraphFunction() {}
Status GraphFunction::GetFunctionDef(const FunctionDef **fdef) {
  *fdef = &(func_record_->fdef());
  return absl::OkStatus();
}
}  // namespace graph
}  // namespace tracing

    FunctionDef fdef_to_load = fdef;
    if (mutate_proto_func) {
      (*mutate_proto_func)(&fdef_to_load);
    }
    VLOG(1) << "Adding func to graph: " << fdef_to_load.DebugString();
    std::vector<char> binary_proto_buf(fdef_to_load.ByteSizeLong());
    fdef_to_load.SerializeToArray(binary_proto_buf.data(),
                                  binary_proto_buf.size());
    TF_Function* func = TF_FunctionImportFunctionDef(

void TestRemoteExecuteSilentCopiesOp(bool async, bool remote,
                                     bool remote_func_outputs = false) {
  return TestRemoteExecuteSilentCopies(async, remote, /*func=*/false,
                                       /*heavy_load_on_streaming_rpc=*/false,
                                       remote_func_outputs);
}

TEST(CAPI, RemoteExecuteSilentCopies) {

  }
  const auto len = std::min(max_func, static_cast<int>(lib.function_size()));
  for (int i = 0; i < len; ++i) {
    TF_Function* func = new TF_Function();
    func->record = new tensorflow::FunctionRecord(lib.function(i), {}, false);
    funcs[i] = func;
  }
  status->status = absl::OkStatus();
  return len;
}

void TF_FunctionToFunctionDef(TF_Function* func, TF_Buffer* output_func_def,

  params->optimizer_configs->struct_size = TP_OPTIMIZER_CONFIGS_STRUCT_SIZE;
  params->optimizer->struct_size = TP_OPTIMIZER_STRUCT_SIZE;
  params->optimizer->create_func = nullptr;
  params->optimizer->optimize_func = optimize_func;
  params->optimizer->destroy_func = nullptr;
}

TEST(Grappler, SuccessfulRegistration) {
  auto plugin_init = [](TP_OptimizerRegistrationParams* const params,

      [plugin_resource_name, create_func, create_func_args,
       delete_func](tensorflow::PluginResource** new_resource) {
        void* opaque_plugin_resource = create_func(create_func_args);
        *new_resource = new tensorflow::PluginResource(
            opaque_plugin_resource, plugin_resource_name, delete_func);
        return tensorflow::OkStatus();
      });

  if (cc_status.ok()) {

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

  TFE_Op* func = TFE_NewOp(ctx, "AddVariablesFunction", status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_OpAddInput(func, packed_handle, status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  if (remote) {
    TFE_OpSetDevice(func, task1_name, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  }

  value = -1;

  // Read our first value
  cache2.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 0);
  EXPECT_EQ(num_compute_calls, 1);
  // Re-read, exepct no additional function compute_func calls.
  cache2.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 0);
  EXPECT_EQ(num_compute_calls, 1);