code: def check_equal(graph, tf_out, input_map=None):
      with testing.disable_span_filling():
          mod, params = from_tensorflow(
              graph.as_graph_def(add_shapes=True)
          )
      with testing.enable_span_filling():
          mod_with_span, _ = from_tensorflow(
              graph.as_graph_def(add_shapes=True)
          )
      assert ir.structural_equal(mod["main"], mod_with_span["main"])
  
      if input_map is not None:
          params.update(input_map)
      relay_out = relay.create_executor("vm", mod=mod).evaluate()(
          **params
      )
      if isinstance(relay_out, nd.NDArray):
          np.testing.assert_allclose(tf_out, relay_out.numpy())
      else:
          if not isinstance(tf_out, (list, tuple)):
              tf_out = [tf_out]
          for x, y in zip(tf_out, [r.numpy() for r in relay_out]):
              np.testing.assert_allclose(x, y)
  
imports: import pytest
  from tensorflow.python.ops import control_flow_ops
  import numpy
  from tvm import nd, relay, ir, testing
  from tvm.relay.frontend.tensorflow import from_tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: check_equal(graph, tf_out, input_map) without return types