
The next step is to fill the `dependencies_` map in the `GraphTask` object since the engine must know when it can execute a task. The `outputs` here is the `inputs` argument passed to the `torch.autograd.backward()` call in Python. But here, we have reversed the names since the gradients w.r.t. the inputs of the forward pass are now the outputs of the backward pass. And from now on, there is no concept of forward/backward, but only graph traversal and execution.

```c++
  auto min_topo_nr = compute_min_topological_nr(outputs);
  // Now compute the dependencies for all executable functions
  compute_dependencies(graph_root.get(), *graph_task, min_topo_nr);

  if (!outputs.empty()) {
    graph_task->init_to_execute(*graph_root, outputs, accumulate_grad, min_topo_nr);
  }

