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 */

	sh_addralign uint64 /* Section alignment */
	sh_entsize   uint64 /* Entry size if section holds table */
}

type elfDyn struct {
	d_tag int64  /* Dynamic entry type */
	d_val uint64 /* Integer value */
}

type elfVerdaux struct {
	vda_name uint32 /* Version or dependency names */
	vda_next uint32 /* Offset in bytes to next verdaux entry */

	sh_addralign uint32 /* Section alignment */
	sh_entsize   uint32 /* Entry size if section holds table */
}

type elfDyn struct {
	d_tag int32  /* Dynamic entry type */
	d_val uint32 /* Integer value */
}

type elfVerdaux struct {
	vda_name uint32 /* Version or dependency names */
	vda_next uint32 /* Offset in bytes to next verdaux entry */

  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()

        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(

	info.symstrings = nil
	info.symtab = nil
	info.versym = nil
	info.verdef = nil
	for i := 0; dyn[i].d_tag != _DT_NULL; i++ {
		dt := &dyn[i]
		p := info.loadOffset + uintptr(dt.d_val)
		switch dt.d_tag {
		case _DT_STRTAB:
			info.symstrings = (*[vdsoSymStringsSize]byte)(unsafe.Pointer(p))
		case _DT_SYMTAB:
			info.symtab = (*[vdsoSymTabSize]elfSym)(unsafe.Pointer(p))
		case _DT_HASH:

  // CHECK: "tf.IfRegion"(%[[PRED]]) <{is_stateless = false}> ({
  %case_op = "tf.IfRegion"(%arg0) <{is_stateless = false}> ({
      // CHECK: %[[TA_VAL:.*]] = "tf.ReadVariableOp"(%[[TA_BUFFER]])
      // CHECK: "tf.Slice"(%[[TA_VAL]]
      // CHECK-NOT: tf.TensorArrayReadV3
      %idx = "tf.Const"() {value = dense<3> : tensor<i32>} : () -> tensor<i32>

// Replace
//   (float)OneHot(index, depth, on_val, off_val, axis)
// With
//   OneHot(index, depth, (float)on_val, (float)off_val, axis)
def FuseOneHotAndCastToFloat : Pat<
  (TFL_CastOp:$output (TFL_OneHotOp $indices,
                                    $depth,
                                    (Arith_ConstantOp $on_val),
                                    (Arith_ConstantOp $off_val),

  def res_name(self, ns, types_ns, name):
    name_str = str(name)
    if name_str in TFR_BUILTINS:
      return {TFRTypes.TFR_BUILTIN_FUNC}, name_str
    if name_str in ns:
      ns_val = ns[name_str]
      return {type(ns_val)}, ns_val
    if name_str in __builtins__:
      return {TFRTypes.PY_BUILTIN_FUNC}, __builtins__[name_str]
    # This name is not in the namespace because the autograph transformation

          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(