const ForwardOperation& op,
    std::unique_ptr<GradientFunction>* gradient_function) const {
  auto iter = registry_.find(op.op_name);
  if (iter == registry_.end()) {
    const string error_msg = "No gradient defined for op: " + op.op_name + ".";
    return errors::NotFound(error_msg);
  }
  gradient_function->reset(iter->second(op));
  return absl::OkStatus();
}

    return;
  }
  std::vector<tensorflow::CoordinatedTask> task_vec(tasks.length);
  auto* task_iter = static_cast<const tensorflow::CoordinatedTask*>(tasks.data);
  for (size_t i = 0; i < tasks.length; ++i) {
    task_vec[i].set_job_name(task_iter->job_name());
    task_vec[i].set_task_id(task_iter->task_id());
    ++task_iter;
  }
  auto results = coord_agent->GetTaskState(task_vec);
  if (!results.ok()) {

  if (!status->status.ok()) return -1;
  auto iter = name_ranges.find(arg_name);
  if (iter == name_ranges.end()) {
    status->status = InvalidArgument("Output arg '", arg_name, "' not found");
    return -1;
  }
  return iter->second.second - iter->second.first;
}

int TF_OperationNumInputs(TF_Operation* oper) {
  return oper->node.num_inputs();
}

                                    " into function '", fn_name, "'");

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;
      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {
        return InvalidArgument("TF_Output ", node->name(), ":", idx,

  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;

    for (const OpDef::ArgDef& arg : fdef.signature().output_arg()) {
      const auto& iter = fdef.ret().find(arg.name());
      if (iter != fdef.ret().end()) {
        const auto& v = a_edges.insert({iter->second, arg.name()});
        ASSERT_TRUE(v.second) << "Duplicate edge " << iter->second << " -> "
                              << arg.name() << ". fdef: " << fdef.DebugString();
      } else {

  Status Init(
      const tensorflow::RewriterConfig_CustomGraphOptimizer* config) override {
    return OkStatus();
  }
  Status Optimize(Cluster* cluster, const GrapplerItem& item,
                  GraphDef* optimized_graph_def) override;

  ~CGraphOptimizer() override {
    if (optimizer_.destroy_func != nullptr) {
      (*optimizer_.destroy_func)(c_optimizer_);
    }
  }

 private:

namespace grappler {

Status CGraphOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
                                 GraphDef* optimized_graph_def) {
  OwnedTFStatus c_status(TF_NewStatus());
  OwnedTFBuffer graph_buf(TF_NewBuffer());
  OwnedTFBuffer optimized_graph_buf(TF_NewBuffer());
  TF_RETURN_IF_ERROR(MessageToBuffer(item.graph, graph_buf.get()));

  optimizer_.optimize_func(c_optimizer_, graph_buf.get(),

TF_CAPI_EXPORT extern void TF_GetNodesToPreserveList(
    const TF_GrapplerItem* item, char** values, size_t* lengths, int num_values,
    void* storage, size_t storage_size, TF_Status* status);

// Get a set of node names for fetch nodes. Fills in `values` and `lengths`,
// they will be used in `TF_GetFetchNodesList`
TF_CAPI_EXPORT extern void TF_GetFetchNodesListSize(const TF_GrapplerItem* item,
                                                    int* num_values,

        "//tensorflow/c:tf_status",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/core:framework",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler/costs:graph_properties",
        "//tensorflow/core/grappler/optimizers:custom_graph_optimizer",
        "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry",