{&obj.Addr{Type: obj.TYPE_ADDR, Name: obj.NAME_AUTO}, Yiauto},
		{&obj.Addr{Type: obj.TYPE_ADDR, Name: obj.NAME_PARAM}, Yiauto},
		{&obj.Addr{Type: obj.TYPE_ADDR, Name: obj.NAME_EXTERN}, Yiauto},
		{&obj.Addr{Type: obj.TYPE_ADDR, Sym: &obj.LSym{Name: "runtime.duff"}}, Yi32},
		{&obj.Addr{Type: obj.TYPE_ADDR, Offset: 4}, Yu7},
		{&obj.Addr{Type: obj.TYPE_ADDR, Offset: 255}, Yu8},

		{immAddr(0), Yi0},

		if a.Scale != 0 || a.Name != 0 || a.Sym != nil {
			break
		}
		return

	case TYPE_ADDR:
		if a.Val != nil {
			break
		}
		if a.Reg == 0 && a.Index == 0 && a.Scale == 0 && a.Name == 0 && a.Sym == nil {
			ctxt.Diag("argument is TYPE_ADDR, should be TYPE_CONST, in %v", p)
		}
		return

	case TYPE_SHIFT, TYPE_REGREG:

    case AttrValue::kB:
      return builder->getBoolAttr(value.b());
    case AttrValue::kType: {
      mlir::Type type;
      TF_RETURN_IF_ERROR(ConvertDataType(value.type(), *builder, &type));
      return mlir::TypeAttr::get(type);
    }
    case AttrValue::kShape:
      return ConvertTensorShapeProto(value.shape(), builder->getContext());
    case AttrValue::kTensor:
      return ConvertTensorProto(value.tensor(), builder);

// before the op being constant folded. Since the constant
// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "quant.qcast" op is used to preserve
// the quantization parameters as a TypeAttr and "quant.dcast" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst              input
//  \       |             |                     |

// before the op being constant folded. Since the constant
// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "tfl.quantize" op is used to preserve
// the quantization parameters as a TypeAttr and "tfl.dequantize" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst          input   min cst       max cst

// index.
template <typename LstmOp>
inline QuantizedType GetIntermediateElementType(LstmOp op, int tensor_index) {
  if (tensor_index < 0 || tensor_index > 4) return nullptr;
  TypeAttr attr = op->template getAttrOfType<TypeAttr>(
      intermediate_attributes[tensor_index]);
  if (!attr) {
    return nullptr;
  }
  return QuantizedType::getQuantizedElementType(attr.getValue());
}

// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "tfl.quantize" op is used to preserve
// the quantization parameters as a TypeAttr and "tfl.dequantize" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst          input   min cst       max cst

          builder.setInsertionPoint(assign_variable_op);
          auto new_q_op = builder.create<QuantizeOp>(
              assign_variable_op.getLoc(), ref_qtype, dq_op.getInput(),
              TypeAttr::get(ref_qtype));
          auto new_assign_variable_op = builder.create<AssignVariableOp>(
              assign_variable_op.getLoc(), assign_variable_op.getResourceId(),
              new_q_op.getResult());

	var x [1]struct{}
	_ = x[TYPE_NONE-0]
	_ = x[TYPE_BRANCH-1]
	_ = x[TYPE_TEXTSIZE-2]
	_ = x[TYPE_MEM-3]
	_ = x[TYPE_CONST-4]
	_ = x[TYPE_FCONST-5]
	_ = x[TYPE_SCONST-6]
	_ = x[TYPE_REG-7]
	_ = x[TYPE_ADDR-8]
	_ = x[TYPE_SHIFT-9]
	_ = x[TYPE_REGREG-10]
	_ = x[TYPE_REGREG2-11]
	_ = x[TYPE_INDIR-12]
	_ = x[TYPE_REGLIST-13]
	_ = x[TYPE_SPECIAL-14]
}

    if (input_types.size() <= 1) return;

    Type target_input_type = mlir::cast<TypeAttr>(input_types[0]).getValue();
    auto result_type = UnrankedTensorType::get(target_input_type);
    for (auto i = 1; i < input_types.size(); ++i) {
      Type current_input_type = mlir::cast<TypeAttr>(input_types[i]).getValue();
      if (current_input_type != target_input_type) {
        input_values[i] =