return ToOperation(iter->second);
  }
}

TF_Operation* TF_GraphNextOperation(TF_Graph* graph, size_t* pos) {
  if (*pos == 0) {
    // Advance past the first sentinel nodes in every graph (the source & sink).
    *pos += 2;
  } else {
    // Advance to the next node.
    *pos += 1;
  }

  mutex_lock l(graph->mu);
  while (*pos < static_cast<size_t>(graph->graph.num_node_ids())) {

#   to transfer ownership to your user.
#
# Docker is enabled by default on Linux. You may disable it if you prefer:
# export TFCI=py311,linux_x86,no_docker

# Advanced: Use Remote Build Execution (RBE) (internal developers only)
#
#   RBE dramatically speeds up builds and testing. It also gives you a
#   public URL to share your build results with collaborators. However,

    *   SavedModels can now save custom gradients. Use the option
        `tf.saved_model.SaveOption(experimental_custom_gradients=True)` to
        enable this feature. The documentation in
        [Advanced autodiff](https://www.tensorflow.org/guide/advanced_autodiff#custom_gradients)
        has been updated.
    *   Object metadata has now been deprecated and no longer saved to the
        SavedModel.

*   TF Core: