n.Args = list
	case *ir.ReturnStmt:
		n.Results = list
	case *ir.AssignListStmt:
		if n.Op() != ir.OAS2FUNC {
			base.Fatalf("rewriteMultiValueCall: invalid op %v", n.Op())
		}
		as.SetOp(ir.OAS2FUNC)
		n.SetOp(ir.OAS2)
		n.Rhs = make([]ir.Node, len(list))
		for i, tmp := range list {
			n.Rhs[i] = AssignConv(tmp, n.Lhs[i].Type(), "assignment")
		}
	}
}

        listenerManager.getBroadcaster(BuildListener).buildFinished(Stub(BuildResult))
    }

    interface Params extends BuildServiceParameters {
        String getProp()

        void setProp(String value)
    }

    static abstract class ServiceImpl implements BuildService<Params> {
        static List<ServiceImpl> instances = []

        String getProp() {
            return getParameters().prop

        TensorShapeProto shape_proto;
        context->ShapeHandleToProto(handle, &shape_proto);
        if (!shape_proto.unknown_rank()) {
          NodeDef const_def;
          const_def.set_op("Const");
          Node* var_node;
          TF_RETURN_IF_ERROR(n->input_node(0, &var_node));
          const_def.set_name(
              graph->NewName(absl::StrCat("var_shape_", var_node->name())));

  if (!name.empty())
    node_def->set_name(std::string(ParseTensorName(name.str()).node()));
  else
    node_def->set_name(
        std::string(op_to_name_.GetUniqueName(func.getName().str())));

  node_def->set_op(FunctionLibraryDefinition::kArgOp);

  mlir::TensorType arg_type = mlir::cast<mlir::TensorType>(arg.getType());
  if (auto resource_type =
          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {

  if (!name.empty())
    node_def->set_name(std::string(ParseTensorName(name.str()).node()));
  else
    node_def->set_name(
        std::string(op_to_name_.GetUniqueName(func.getName().str())));

  node_def->set_op(FunctionLibraryDefinition::kArgOp);

  mlir::TensorType arg_type = mlir::cast<mlir::TensorType>(arg.getType());
  if (auto resource_type =
          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {

    const absl::flat_hash_map<absl::string_view, int>& histogram) {
  for (const auto& pair : histogram) {
    XlaAutoClusteringSummary::OpAndCount* new_entry = result->Add();
    new_entry->set_op(std::string(pair.first));
    new_entry->set_count(pair.second);
  }

  absl::c_sort(*result, [](const XlaAutoClusteringSummary::OpAndCount& a,
                           const XlaAutoClusteringSummary::OpAndCount& b) {

    TF_ASSIGN_OR_RETURN(auto tf_name,
                        GetTensorFlowOpName(inst->getName().getStringRef()));
    op_name.append(tf_name);
  }

  node_def->set_name(name.str());
  node_def->set_op(std::string(op_name.str()));

  // Update NodeDef constructed out of an MLIR Case/If/While op to map it to
  // either TensorFlow StatelessX or X op depending on the additional attribute.

  const int num_inputs = input_shapes->num_items;
  NodeDef node_def;
  tensorflow::ImmediateExecutionOperation* op = tensorflow::unwrap(tfe_op);
  node_def.set_name(op->Name());
  node_def.set_op(op->Name());
  for (int i = 0; i < num_inputs; ++i) {
    node_def.add_input("dummy_input");
  }
  OperationFromInterface(op)->Attrs().FillAttrValueMap(node_def.mutable_attr());

	// same type as the variable on the lhs.
	if ok := n.Lhs[1]; !ir.IsBlank(ok) && ok.Type().IsBoolean() {
		call.Type().Field(1).Type = ok.Type()
	}
	n.Rhs = []ir.Node{call}
	n.SetOp(ir.OAS2FUNC)

	// don't generate a = *var if a is _
	if ir.IsBlank(a) {
		return walkExpr(typecheck.Stmt(n), init)
	}

	var_ := typecheck.TempAt(base.Pos, ir.CurFunc, types.NewPtr(t.Elem()))

	// because walkExpr will lower the string
	// conversion into a runtime call.
	// See issue 24937 for more discussion.
	if cond.Op() == ir.OBYTES2STR && allCaseExprsAreSideEffectFree(sw) {
		cond := cond.(*ir.ConvExpr)
		cond.SetOp(ir.OBYTES2STRTMP)
	}

	cond = walkExpr(cond, sw.PtrInit())
	if cond.Op() != ir.OLITERAL && cond.Op() != ir.ONIL {
		cond = copyExpr(cond, cond.Type(), &sw.Compiled)
	}

	base.Pos = lno

	s := exprSwitch{