}
  // Avoid a refcount bump for the Future, since we check for refcount in
  // DistEngine (see TORCH_INTERNAL_ASSERT(futureGrads.use_count() == 1)
  // in dist_engine.cpp).
  auto& fut = graph_task->future_result_;
  fut->wait();
  return fut->value().toTensorVector();
}
```
And now, we are ready to start the actual execution by creating the InputBuffer. In case we only have one root variable, we begin by copying the value of the inputs tensor (this is the gradients passed to python backward) in position 0 of the input_buffer. This is a small optimization that avoids running the RootNode for no reason. Also, if the rest of the graph is not on the cpu, we directly start on that worker while the RootNode is always placed on the cpu ready queue. Details of the workers and ready queues are explained in the section below.
On the other hand, if we have multiple roots, the GraphRoot object also holds the inputs, so it is enough to pass it an empty InputBuffer.