auto r = tsl::core::RefCountPtr<tensorflow::IntraProcessRendezvous>(
      new tensorflow::IntraProcessRendezvous(device_mgr.get()));
  tensorflow::EagerContext* eager_context = new tensorflow::EagerContext(
      opts->session_options.options,
      static_cast<tensorflow::ContextDevicePlacementPolicy>(
          opts->device_placement_policy),
      opts->async, device_mgr.release(),

  auto* gpu_options = config.mutable_gpu_options();
  gpu_options->set_allow_growth(gpu_memory_allow_growth);

  (*config.mutable_device_count())["CPU"] = num_cpu_devices;

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be

  // Build an ExecutionContext from the HostContext.
  auto builder = RequestContextBuilder(host_context_.get(), &resource_context_);

  // Get tensorflow::EagerContext for the kernel fallback.
  auto* eager_context_resource =
      resource_context_
          .GetOrCreateResource<tensorflow::tfd::EagerContextResource>(
              tensorflow::tfd::kEagerContextResourceName);

  if (!s.ok()) {
    tsl::Set_TF_Status_from_Status(status, s);
    return "// error";
  }

  const std::string& function_name = functiondef.signature().name();
  EagerContext* cpp_context = ContextFromInterface(unwrap(tfe_context));
  FunctionLibraryDefinition& flib_def = *cpp_context->FuncLibDef();
  const tensorflow::FunctionDef* fdef = flib_def.Find(function_name);
  if (fdef == nullptr) {

}

void TFE_ContextDisableGraphCollection(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(false);
}

uint64_t TFE_GetContextId(TFE_Context* ctx) {
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  return context->GetContextId();
}

void TFE_MonitoringCounterCellIncrementBy(TFE_MonitoringCounterCell* cell,