DefaultToolchain toolchain = newDefaultToolchain(model, "TYPE");
        assertEquals("TYPE", toolchain.getType());

        model.setType("MODEL_TYPE");
        toolchain = newDefaultToolchain(model);
        assertEquals("MODEL_TYPE", toolchain.getType());
    }

    @Test
    void testGetLogger() {
        ToolchainModel model = new ToolchainModel();

        public ModelView<ModelSet<T>> toView(MutableModelNode modelNode, ModelRuleDescriptor ruleDescriptor, boolean mutable) {
            ModelType<ModelSet<T>> setType = ModelTypes.modelSet(elementType);
            DefaultModelViewState state = new DefaultModelViewState(modelNode.getPath(), setType, ruleDescriptor, mutable, !mutable);

        toolchain.setType("TYPE");
        toolchain.setProvides(props);
        PersistedToolchains userResult = new PersistedToolchains();
        userResult.setToolchains(Collections.singletonList(toolchain));

        props = new Properties();
        props.put("key", "global_value");
        toolchain = new ToolchainModel();
        toolchain.setType("TYPE");
        toolchain.setProvides(props);

  return absl::OkStatus();
}

Status MlirAbstractOp::AddRef(Type type, Type* output_type) {
  Type elt_type = getElementTypeOrSelf(type);
  if (mlir::isa<mlir::TF::TensorFlowRefType>(elt_type)) {
    return InvalidArgument("Requested reference to a reference type");
  }
  elt_type = TensorFlowRefType::get(elt_type);
  if (RankedTensorType tensor_type = mlir::dyn_cast<RankedTensorType>(type)) {

	}
	ptr = ir.NewUnaryExpr(base.Pos, ir.OSPTR, n)
	if n.Type().IsString() {
		ptr.SetType(types.Types[types.TUINT8].PtrTo())
	} else {
		ptr.SetType(n.Type().Elem().PtrTo())
	}
	ptr.SetTypecheck(1)
	length = ir.NewUnaryExpr(base.Pos, ir.OLEN, n)
	length.SetType(types.Types[types.TINT])
	length.SetTypecheck(1)
	return ptr, length
}

                              .Device(DEVICE)                                  \
                              .HostMemory("output")                            \
                              .TypeConstraint<int32>("out_type")               \
                              .TypeConstraint("T", TYPES),                     \
                          ShapeOp<int32>);                                     \

		// if res != nil {
		//    res = res.type
		// }

		// Grab its parts.
		itab := ir.NewUnaryExpr(base.Pos, ir.OITAB, c)
		itab.SetType(types.Types[types.TUINTPTR].PtrTo())
		itab.SetTypecheck(1)
		data := ir.NewUnaryExpr(n.Pos(), ir.OIDATA, c)
		data.SetType(types.Types[types.TUINT8].PtrTo()) // Type is generic pointer - we're just passing it through.
		data.SetTypecheck(1)

	var buf bytes.Buffer
	w := dummyWriter(&buf)

	var s Sym
	s.SetABI(1)
	s.SetType(uint8(objabi.STEXT))
	s.SetFlag(0x12)
	s.SetSiz(12345)
	s.SetAlign(8)
	s.Write(w)

	var r Reloc
	r.SetOff(12)
	r.SetSiz(4)
	r.SetType(uint16(objabi.R_ADDR))
	r.SetAdd(54321)
	r.SetSym(SymRef{11, 22})
	r.Write(w)

	var a Aux
	a.SetType(AuxFuncInfo)
	a.SetSym(SymRef{33, 44})
	a.Write(w)

	w.wr.Flush()

			n.SetType(nil)
			return n
		}
		if !n.Y.Type().IsBoolean() {
			base.Errorf("invalid operation: %v (operator %v not defined on %s)", n, n.Op(), typekind(n.Y.Type()))
			n.SetType(nil)
			return n
		}
		l, r, t := tcArith(n, n.Op(), n.X, n.Y)
		n.X, n.Y = l, r
		n.SetType(t)
		return n

	// shift operators
	case ir.OLSH, ir.ORSH:

  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;
  saved_variable.set_dtype(dtype);
  shape.AsProto(saved_variable.mutable_shape());

  std::unique_ptr<Variable> var;
  TF_EXPECT_OK(internal::LoadSavedVariable(context(), saved_variable, &var));
  EXPECT_EQ(var->dtype(), dtype);