// Input tensors
    const std::vector<std::pair<string, Tensor>>& input_pairs,
    // Output tensors
    const std::vector<string>& output_tensor_names, TF_Tensor** c_outputs,
    // Target nodes
    const std::vector<string>& target_oper_names, TF_Buffer* run_metadata,
    TF_Status* status) {
  const int noutputs = output_tensor_names.size();
  std::vector<Tensor> outputs(noutputs);
  Status result;

// manual user-provided gradients.
class GradientCheckerTest
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());

    {
      absl::Status s = StatusFromTF_Status(status.get());

Vendoring in std and cmd
========================

The Go command maintains copies of external packages needed by the
standard library in the src/vendor and src/cmd/vendor directories.

There are two modules, std and cmd, defined in src/go.mod and
src/cmd/go.mod. When a package outside std or cmd is imported
by a package inside std or cmd, the import path is interpreted
as if it had a "vendor/" prefix. For example, within "crypto/tls",

    return nullptr;
  }
  std::vector<std::unique_ptr<tensorflow::Device>> devices;
  status->status = tensorflow::DeviceFactory::AddDevices(
      opts->session_options.options, "/job:localhost/replica:0/task:0",
      &devices);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<tensorflow::DeviceMgr> device_mgr(
      new tensorflow::DynamicDeviceMgr(std::move(devices)));

void TFE_GetExecutedOpNames(TFE_Context* ctx, TF_Buffer* buf,
                            TF_Status* status) {
  const std::vector<std::string>& op_names =
      tensorflow::unwrap(ctx)->GetLoggedOpsTestonly();

  std::ostringstream op_names_oss;
  for (const auto& op : op_names) {
    op_names_oss << op << ", ";
  }
  const std::string& op_names_str = op_names_oss.str();
  void* data = tensorflow::port::Malloc(op_names_str.length());

  TF_DeleteBuffer(buffer);
  return ret;
}

std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());
  }
  std::sort(grads.begin(), grads.end());
  return grads;
}

// gradient registerer to instantiate a GradientFunction.
struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;
  std::vector<AbstractTensorHandle*> outputs;
  std::vector<int64_t> skip_input_indices;
  AttrBuilder attrs;
};

using GradientFunctionFactory =
    std::function<GradientFunction*(const ForwardOperation& op)>;

// Map from op name to a `GradientFunctionFactory`.

  virtual int DeviceId(absl::Status* status) const = 0;
  // Returns a tensor for the handle. If tensor is remote, it will be copied.
  virtual AbstractTensorInterface* Resolve(absl::Status* status) = 0;

  std::string DebugString() const override;

  // Returns a Boolean hint indicating whether callers should prefer
  // `SummarizeValue` to resolving this handle and formatting the tensor.
  //

  absl::Status Run(const std::string& function_name,
                   const tensorflow::DeviceSet& device_set,
                   const tensorflow::ConfigProto& config_proto,
                   const FunctionOptions& function_options,
                   std::unique_ptr<tensorflow::Graph>* graph,
                   tensorflow::FunctionLibraryDefinition* flib_def,
                   std::vector<std::string>* control_ret_node_names,

--- /dt9/usr/include/x86_64-linux-gnu/sys/cdefs.h       2013-09-30 13:58:17.000000000 +0000
+++ /dt9/usr/include/x86_64-linux-gnu/sys/cdefs.new.h   2022-11-04 17:17:31.727061220 +0000
@@ -320,7 +320,7 @@

 /* GCC 4.3 and above with -std=c99 or -std=gnu99 implements ISO C99
    inline semantics, unless -fgnu89-inline is used.  */
-#if (!defined __cplusplus || __GNUC_PREREQ (4,3)) && defined __GNUC__