opregreg(s, v.Op.Asm(), v.Reg(), v.Args[0].Reg())
	case ssa.Op386DUFFZERO:
		p := s.Prog(obj.ADUFFZERO)
		p.To.Type = obj.TYPE_ADDR
		p.To.Sym = ir.Syms.Duffzero
		p.To.Offset = v.AuxInt
	case ssa.Op386DUFFCOPY:
		p := s.Prog(obj.ADUFFCOPY)
		p.To.Type = obj.TYPE_ADDR
		p.To.Sym = ir.Syms.Duffcopy
		p.To.Offset = v.AuxInt

	case ssa.OpCopy: // TODO: use MOVLreg for reg->reg copies instead of OpCopy?

      // Here TArgs types do not include types of the first two parameters,
      // i.e. the convolution input and the filter. TArgs are parameters for
      // the extras like the bias etc.
      auto attr = TypeAttr::get(getElementTypeOrSelf(contraction.getType()));
      SmallVector<Attribute, 4> targs_values(operands.size() - 2, attr);

      ArrayAttr targs_attr = ArrayAttr::get(context, targs_values);
      attrs.push_back(

        auto min_max = GetMinMaxValuesForArgument(func_name, i);
        // The input min/max or mean/std are not specified, then skip.
        if (!min_max.first.has_value() || !min_max.second.has_value()) return;

        TypeAttr params = quant::GetQuantizedTypeAttr(
            builder, input_type, builder.getF64FloatAttr(min_max.first.value()),
            builder.getF64FloatAttr(min_max.second.value()),

			if v.AuxInt != 0 || v.Args[0].Reg() != v.Reg() {
				p := s.Prog(ppc64.AMOVD)
				p.From.Type = obj.TYPE_ADDR
				p.From.Reg = v.Args[0].Reg()
				p.From.Offset = v.AuxInt
				p.To.Type = obj.TYPE_REG
				p.To.Reg = v.Reg()
			}

		case *obj.LSym, ir.Node:
			p := s.Prog(ppc64.AMOVD)
			p.From.Type = obj.TYPE_ADDR
			p.From.Reg = v.Args[0].Reg()
			p.To.Type = obj.TYPE_REG
			p.To.Reg = v.Reg()

	// internally defined symbols.
	if p.From.Type == obj.TYPE_ADDR && p.From.Name == obj.NAME_EXTERN && !p.From.Sym.Local() {
		// MOVV $sym, Rx becomes MOVV sym@GOT, Rx
		// MOVV $sym+<off>, Rx becomes MOVV sym@GOT, Rx; ADD <off>, Rx
		if p.As != AMOVV {
			ctxt.Diag("do not know how to handle TYPE_ADDR in %v with -shared", p)
		}
		if p.To.Type != obj.TYPE_REG {

  // Reshape for dynamic shaped operands. The result shape is
  // [lhs_batch_dimensions, lhs_out_dimensions, rhs_out_dimensions].
  BoolAttr true_attr = rewriter.getBoolAttr(true);
  auto lhs_shape = rewriter.create<TF::ShapeOp>(loc, lhs, true_attr);
  auto rhs_shape = rewriter.create<TF::ShapeOp>(loc, rhs, true_attr);
  llvm::SmallVector<int64_t, 4> lhs_batch_and_out =
      Concat<int64_t>(lhs_dot_dimensions_info.batch_dimensions().AxesArray(),

        auto min_max = GetMinMaxValuesForArgument(func_name, i);
        // The input min/max or mean/std are not specified, then skip.
        if (!min_max.first.has_value() || !min_max.second.has_value()) return;

        TypeAttr params = quant::GetQuantizedTypeAttr(
            builder, input_type, builder.getF64FloatAttr(min_max.first.value()),
            builder.getF64FloatAttr(min_max.second.value()),

  if (node.IsArg()) {
    if (dtype == DT_RESOURCE) {
      const AttrValue* dtype_attr = node.attrs().Find("_handle_dtypes");
      const AttrValue* shape_attr = node.attrs().Find("_handle_shapes");
      if (dtype_attr && shape_attr) {
        if (dtype_attr->list().type().empty()) {
          return errors::InvalidArgument(

            res_types, res_attrs)))
      return failure();

    // Update the function type which has excluded the control args.
    func->setAttr("function_type", TypeAttr::get(rewriter.getFunctionType(
                                       arg_types, res_types)));

    // Update arg/result attributes.
    func.setAllArgAttrs(arg_attrs);
    func.setAllResultAttrs(res_attrs);

func (p *Parser) symbolReference(a *obj.Addr, name string, prefix rune) {
	// Identifier is a name.
	switch prefix {
	case 0:
		a.Type = obj.TYPE_MEM
	case '$':
		a.Type = obj.TYPE_ADDR
	case '*':
		a.Type = obj.TYPE_INDIR
	}

	// Parse optional <> (indicates a static symbol) or
	// <ABIxxx> (selecting text symbol with specific ABI).
	doIssueError := true