code: def test_annotate_spans_compatibility():
      data = relay.var(
          "data", relay.TensorType((1, 3, 64, 64), "float32")
      )
      weight = relay.var("weight")
  
      bn_gamma = relay.var("bn_gamma")
      bn_beta = relay.var("bn_beta")
      bn_mmean = relay.var("bn_mean")
      bn_mvar = relay.var("bn_var")
  
      simple_net = relay.nn.conv2d(
          data=data,
          weight=weight,
          kernel_size=(3, 3),
          channels=3,
          padding=(1, 1),
      )
      simple_net = relay.nn.batch_norm(
          simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar
      )[0]
      simple_net = relay.Function(
          relay.analysis.free_vars(simple_net), simple_net
      )
  
      module, params = testing.create_workload(simple_net)
  
      # Apply some simple passes to legalize the IR.
      with tvm.transform.PassContext(opt_level=0):
          module, params = relay.optimize(
              module,
              target=tvm.testing.enabled_targets()[0][0],
              params=params,
          )
  
      seq = tvm.transform.Sequential(
          [relay.transform.AnnotateSpans(), relay.transform.DefuseOps()]
      )
      with tvm.transform.PassContext(opt_level=3):
          module = seq(module)
  
imports: import tvm
  import tvm.relay
  from tvm.relay import testing
  import tvm.testing
label_desc: Use image node_id 1 for calling a global function with example usage: test_annotate_spans_compatibility() without return types