TF_RETURN_IF_ERROR(
        RunAndMaybeSum(ctx, forward, theta_inputs, f_outputs, use_function));
    AbstractTensorHandlePtr fPlus(f_outputs[0]);

    // Get f(theta - eps):
    theta_inputs[input_index] = thetaMinus.get();
    TF_RETURN_IF_ERROR(
        RunAndMaybeSum(ctx, forward, theta_inputs, f_outputs, use_function));
    AbstractTensorHandlePtr fMinus(f_outputs[0]);

	if err != nil {
		return Info{}, err
	}
	reservedBlocks := uint64(s.F_bfree) - uint64(s.F_bavail)
	info = Info{
		Total:  uint64(s.F_bsize) * (uint64(s.F_blocks) - reservedBlocks),
		Free:   uint64(s.F_bsize) * uint64(s.F_bavail),
		Files:  uint64(s.F_files),
		Ffree:  uint64(s.F_ffree),
		FSType: getFSType(s.F_fstypename[:]),
	}
	if info.Free > info.Total {

template <typename F>
class Cleanup {
 public:
  Cleanup() : released_(true), f_() {}

  template <typename G>
  explicit Cleanup(G&& f)          // NOLINT
      : f_(std::forward<G>(f)) {}  // NOLINT(build/c++11)

  Cleanup(Cleanup&& src)  // NOLINT
      : released_(src.is_released()), f_(src.release()) {}

  // Implicitly move-constructible from any compatible Cleanup<G>.

};

class MatMulGradientFunction : public GradientFunction {
 public:
  explicit MatMulGradientFunction(vector<AbstractTensorHandle*> f_inputs,
                                  AttrBuilder f_attrs)
      : forward_inputs_(f_inputs), forward_attrs_(f_attrs) {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Ref();
      }
    }
  }

	var lockType int16
	switch flag {
	case syscall.O_RDONLY:
		lockType = syscall.F_RDLCK
	case syscall.O_WRONLY:
		fallthrough
	case syscall.O_RDWR:
		fallthrough
	case syscall.O_WRONLY | syscall.O_CREAT:
		fallthrough
	case syscall.O_RDWR | syscall.O_CREAT:
		lockType = syscall.F_WRLCK
	default:
		return nil, &os.PathError{
			Op:   "open",
			Path: path,
			Err:  syscall.EINVAL,
		}

namespace tensorflow {
namespace gradients {
namespace {

class ReluGradientFunction : public GradientFunction {
 public:
  explicit ReluGradientFunction(vector<AbstractTensorHandle*> f_outputs)
      : forward_outputs_(f_outputs) {
    for (auto output : forward_outputs_) {
      if (output) {
        output->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,

		}
		if !ps.Participating {
			continue
		}
		// for participating pools, total bytes to be rebalanced by this pool is given by,
		// pf_c = (f_i + x)/c_i,
		// pf_c - percentage free space across pools, f_i - ith pool's free space, c_i - ith pool's capacity
		// i.e. x = c_i*pfc -f_i
		totalBytesToRebal := float64(ps.InitCapacity)*meta.PercentFreeGoal - float64(ps.InitFreeSpace)
		elapsed := time.Since(ps.Info.StartTime)

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_Tensor* f_t = TFE_TensorHandleResolve(final, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  float* f_value = static_cast<float*>(TF_TensorData(f_t));
  ASSERT_EQ(*f_value, 4.0);

  TF_DeleteOutputList(add_outputs);
  TF_DeleteAbstractOp(fn_op);
  TF_DeleteAbstractTensor(input_t);

}

// DisableDirectIO - disables directio mode.
func DisableDirectIO(f *os.File) error {
	fd := f.Fd()
	flag, err := unix.FcntlInt(fd, unix.F_GETFL, 0)
	if err != nil {
		return err
	}
	flag &= ^(syscall.O_DIRECT)
	_, err = unix.FcntlInt(fd, unix.F_SETFL, flag)
	return err
}

// AlignedBlock - pass through to directio implementation.
func AlignedBlock(blockSize int) []byte {

pkg syscall (darwin-arm64), const F_FLUSH_DATA ideal-int
pkg syscall (darwin-arm64), const F_FREEZE_FS = 53
pkg syscall (darwin-arm64), const F_FREEZE_FS ideal-int
pkg syscall (darwin-arm64), const F_FULLFSYNC = 51
pkg syscall (darwin-arm64), const F_FULLFSYNC ideal-int
pkg syscall (darwin-arm64), const F_GETCODEDIR = 72
pkg syscall (darwin-arm64), const F_GETCODEDIR ideal-int