DT_ENCODING     DynTag = 32 /* Values greater than or equal to DT_ENCODING
	   and less than DT_LOOS follow the rules for
	   the interpretation of the d_un union
	   as follows: even == 'd_ptr', even == 'd_val'
	   or none */
	DT_PREINIT_ARRAY   DynTag = 32 /* Address of the array of pointers to pre-initialization functions. */
	DT_PREINIT_ARRAYSZ DynTag = 33 /* Size in bytes of the array of pre-initialization functions. */

// license that can be found in the LICENSE file.

package main

type Elem struct{}

func (*Elem) Wait(callback func()) {}

type Base struct {
	elem [8]*Elem
}

var g_val = 1

func (s *Base) Do() *int {
	resp := &g_val
	for _, e := range s.elem {
		e.Wait(func() {
			*resp = 0
		})
	}
	return resp
}

type Sub struct {
	*Base
}

func main() {
	a := Sub{new(Base)}

	}
	if err != nil {
		return nil, nil, err
	}
	dhVal, err := privEph.ECDH(pubRecipient)
	if err != nil {
		return nil, nil, err
	}
	encPubEph := privEph.PublicKey().Bytes()

	encPubRecip := pubRecipient.Bytes()
	kemContext := append(encPubEph, encPubRecip...)

	return dh.ExtractAndExpand(dhVal, kemContext), encPubEph, nil
}

type Sender struct {
	aead cipher.AEAD

  y = variables.Variable(1.0, name='y')
  updates = control_flow_ops.no_op()
  for _ in range(3):
    with ops.control_dependencies([updates]):
      x_val = x.read_value() + y
      updates = x.assign_sub(0.1 * x_val)

  array_ops.identity(updates, name='result')


def write_graph(build_graph, out_dir):
  """Build a graph using build_graph and write it out."""
  g = ops.Graph()

const (
	_O_RDONLY   = 0x0
	_O_WRONLY   = 0x1
	_O_CREAT    = 0x40
	_O_TRUNC    = 0x200
	_O_NONBLOCK = 0x800
	_O_CLOEXEC  = 0x80000
)

type usigset struct {
	__val [16]uint64
}

type stackt struct {
	ss_sp     *byte
	ss_flags  int32
	pad_cgo_0 [4]byte
	ss_size   uintptr
}

type sigcontext struct {
	fault_address uint64
	/* AArch64 registers */

        Args:
          use_variable: If True, creates a variable for weight.
        """
        super().__init__()
        w_val = np.random.randn(128, 32).astype('f4')
        if use_variable:
          self.w = variables.Variable(w_val)
        else:
          self.w = w_val

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(

const (
	_O_RDONLY   = 0x0
	_O_WRONLY   = 0x1
	_O_CREAT    = 0x40
	_O_TRUNC    = 0x200
	_O_NONBLOCK = 0x800
	_O_CLOEXEC  = 0x80000
)

type usigset struct {
	__val [16]uint64
}

type fpxreg struct {
	significand [4]uint16
	exponent    uint16
	padding     [3]uint16
}

type xmmreg struct {
	element [4]uint32
}

type fpstate struct {

          use_variable: If True, creates a variable for weight.
        """
        super(GatherModel, self).__init__()
        w_val = np.random.randn(128, 32).astype('f4')
        if use_variable:
          self.w = variables.Variable(w_val)
        else:
          self.w = w_val

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(

    // number without knowing this, so we need to manually dispatch to each
    // possible member of TensorProto, depening on its dtype.
#define MATCH(DTYPE, FIELD) \
  case DTYPE:               \
    return tensor.FIELD##_val().size()

  switch (tensor.dtype()) {
    MATCH(DT_FLOAT, float);
    MATCH(DT_DOUBLE, double);
    MATCH(DT_INT8, int);
    MATCH(DT_UINT8, int);
    MATCH(DT_INT16, int);
    MATCH(DT_UINT16, int);

  if (shape_x.size() == shape_y.size()) {
    llvm::SmallVector<int64_t, 4> out_shape(shape_x.size());
    for (int i = 0; i < shape_x.size(); i++) {
      auto x_val = shape_x[i];
      auto y_val = shape_y[i];
      out_shape[i] = std::max(x_val, y_val);
    }
    return tensorflow::GetTypeFromTFTensorShape(out_shape, element_type);
  }

  auto shape_large = shape_x.size() > shape_y.size() ? shape_x : shape_y;