code: def test_type_check_add_true(self):
      class M(torch.nn.Module):
          def forward(
              self, x: TensorType((1, 2, Dyn)), y: TensorType((1, 2, 3))
          ):
              return torch.add(x, y)
  
      module = M()
      symbolic_traced: torch.fx.GraphModule = symbolic_trace(module)
      tc = GraphTypeChecker({}, symbolic_traced)
      self.assertTrue(tc.type_check())
  
      expected_ph_types = [
          TensorType((1, 2, Dyn)),
          TensorType((1, 2, 3)),
      ]
      expected_iter = iter(expected_ph_types)
  
      for n in symbolic_traced.graph.nodes:
          if n.op == "placeholder":
              assert n.type == next(expected_iter)
          if n.op == "output":
              assert n.type == TensorType((1, 2, Dyn))
  
imports: import unittest
  import torch
  from torch.fx import symbolic_trace
  from torch.fx.experimental.unify_refinements import infer_symbolic_types
  from torch.fx.experimental.refinement_types import Equality
  from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
  from torch.fx.annotate import annotate
  from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
  from torch.fx.experimental.rewriter import RewritingTracer
  from torch.fx import GraphModule
  from torch.fx.passes.shape_prop import ShapeProp
  from torch.testing._internal.common_utils import TestCase
  import sympy
label_desc: Use image node_id 4 for calling the TypeCheckerTest obj's underlying member method code with example usage: obj.test_type_check_add_true() and returns: torch