grads.push_back(grad_var);
    } 
  }
```
Now, if the inputs argument was specified in backward or we used the torch.autograd.grad api, the following code creates a list of edges to accumulate the gradients in the specified tensors at the end of the computation. The engine uses this later to optimize the execution as it doesn’t add the gradients in all the leaf nodes, just the specified ones.
```c++
  std::vector output_edges;
  if (inputs != nullptr) {
    int num_inputs = PyTuple_GET_SIZE(inputs);
    output_edges.reserve(num_inputs);
    for (const auto i : c10::irange(num_inputs)) {
      PyObject *input = PyTuple_GET_ITEM(inputs, i);
      const auto& tensor = THPVariable_Unpack(input);
      const auto output_nr = tensor.output_nr();
      auto grad_fn = tensor.grad_fn();
      if (!grad_fn) {
        grad_fn = torch::autograd::impl::try_get_grad_accumulator(tensor);
      }
      if (accumulate_grad) {
        tensor.retain_grad();
      }
      if (!grad_fn) {