code: def test_ref():
      metatable = {"VirtualDevice": [CPU, GPU]}
  
      def input():
          return tvm.relay.parse(
              """
              #[version = "0.0.5"]
              def @main(%x: Tensor[(5, 7), float32], %y: Tensor[(5, 7), float32]) {
                let %r = ref(%x);
                %0 = on_device(%y, virtual_device=meta[VirtualDevice][0]);
                ref_write(%r, %0);
                %1 = ref_read(%r);
                add(%x, %1)
              }
          """,
              "from_string",
              None,
              metatable,
          )
  
      def expected():
          return tvm.relay.parse(
              """
              #[version = "0.0.5"]
              def @main(%x {virtual_device=meta[VirtualDevice][1]}: Tensor[(5, 7), float32], %y {virtual_device=meta[VirtualDevice][0]}: Tensor[(5, 7), float32],
                        virtual_device=meta[VirtualDevice][1]) {
                let %r = on_device(ref(%x), virtual_device=meta[VirtualDevice][1], constrain_result=True);
                %0 = device_copy(%y, src_virtual_device=meta[VirtualDevice][0], dst_virtual_device=meta[VirtualDevice][1]);
                on_device(ref_write(%r, %0), virtual_device=meta[VirtualDevice][1], constrain_result=True);
                %1 = ref_read(%r);
                add(%x, %1)
              }
          """,
              "from_string",
              None,
              metatable,
          )
  
      def ref(x, y):
          r = {"value": x}
          r["value"] = y
          return np.add(x, r["value"])
  
      # Don't try to execute, no backend currently supports both hetrogeneous devices and references.
      exercise(input(), expected(), None, None)
  
imports: import tvm
  from tvm import relay
  from tvm.script import tir
  import tvm.testing
  import numpy
  import os
label_desc: Use image node_id 33 for calling a global function with example usage: test_ref() and returns: tvm, tvm, np