// CHECK: [[OLD_VAR:%.*]] = "tf.ReadVariableOp"([[VAR_HANDLE]]) : (tensor<*x!tf_type.resource<tensor<*xf32>>>) -> tensor<*xf32>
    // CHECK: [[NEW_VAR:%.*]] = "tf.Sub"([[OLD_VAR]], [[VAR_DELTA]])
    // CHECK: "tf.AssignVariableOp"([[VAR_HANDLE]], [[NEW_VAR]])
    // CHECK: "tf.AssignVariableOp"([[M_HANDLE]], [[NEW_M]])
    // CHECK: "tf.AssignVariableOp"([[V_HANDLE]], [[NEW_V]])

               Node** var_handle_op = nullptr) {
  Output var_handle =
      ops::VarHandleOp(scope.WithOpName("Var" + id), DT_FLOAT, TensorShape({}));
  Output read =
      ops::ReadVariableOp(scope.WithOpName("Read" + id), var_handle, DT_FLOAT);
  if (var_handle_op) {
    *var_handle_op = var_handle.node();
  }
  return read.node();
}

    %add2:2 = tf_executor.island wraps "tf.Add"(%arg0, %one#0) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %var_handle:2 = tf_executor.island wraps "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> !tf_res
    %assign = tf_executor.island wraps "tf.AssignVariableOp"(%var_handle, %arg1) : (!tf_res, tensor<f32>) -> ()

            GetXlaClusterForNode(*n)) {
      n->set_name(absl::StrCat(*cluster_name, "/", n->name()));
    } else if (n->type_string() == "VarHandleOp") {
      n->set_name(absl::StrCat("varhandle/", n->name()));
    } else {
      // There is room for improvement here.  In particular, it may help to
      // split these unclustered nodes into classes where every node in a