code: def test_type_check_flatten(self):
      class M(torch.nn.Module):
          def forward(self, x: TensorType((1, 2, 3, 5, Dyn))):
              return torch.flatten(x, 1, 2)
  
      module = M()
      symbolic_traced: torch.fx.GraphModule = symbolic_trace(module)
      tc = GraphTypeChecker({}, symbolic_traced)
      tc.type_check()
      for n in symbolic_traced.graph.nodes:
          if n.op == "output":
              assert n.type == TensorType((1, 6, 5, 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 20 for calling the TypeCheckerTest obj's underlying member method code with example usage: obj.test_type_check_flatten() and returns: torch