if (edge->src_output() == oper_out.index) {
      if (count < max_consumers) {
        consumers[count] = {ToOperation(edge->dst()), edge->dst_input()};
      }
      ++count;
    }
  }
  return count;
}

int TF_OperationNumControlInputs(TF_Operation* oper) {
  int count = 0;
  for (const auto* edge : oper->node.in_edges()) {
    if (edge->IsControlEdge() && !edge->src()->IsSource()) {

  return *factories;
}

static tracing::FactoryFunction default_factory;

void RegisterTracingEngineFactory(const string& name, FactoryFunction factory) {
  assert((!GetFactories().count(name)) ||
         (GetFactories()[name] == factory) &&
             "Duplicate tracing factory registration");
  GetFactories()[name] = factory;
}

Status SetDefaultTracingEngine(const char* name) {

  }

  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

                   TF_Message(status));
    }

    if (device_index == 0) {
      first_op_output_count = per_device_output_tensors.rbegin()->size();
    } else {
      if (first_bad_status == nullptr &&
          per_device_output_tensors.rbegin()->size() != first_op_output_count) {
        first_bad_status.reset(TF_NewStatus());
        TF_SetStatus(first_bad_status.get(), TF_INTERNAL,

                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
                                                        bucket_count);
  });
}

void TFE_MonitoringDeleteBuckets(TFE_MonitoringBuckets* buckets) {
  delete buckets;