code: def test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten(self):
      class BasicBlock(torch.nn.Module):
          def __init__(self):
              super().__init__()
  
              self.conv1 = torch.nn.Conv2d(3, 6, 5)
              self.pool = torch.nn.MaxPool2d(2, 2)
              self.conv2 = torch.nn.Conv2d(6, 16, 5)
              self.fc1 = torch.nn.Linear(5, 120)
              self.pool2 = torch.nn.AdaptiveAvgPool2d((6, 7))
  
          def forward(self, x: TensorType((4, 3, Dyn, Dyn))):
              out = self.conv1(x)
              out = self.pool(out)
              out = self.conv2(out)
              out = self.pool(out)
              out = self.fc1(out)
              out = self.pool2(out)
              out = torch.flatten(out, 1)
              return out
  
      B = BasicBlock()
      ast_rewriter = RewritingTracer()
      traced = symbolic_trace(B)
      tc = GraphTypeChecker({}, traced)
      tc.type_check()
      infer_symbolic_types(traced)
  
      for n in traced.graph.nodes:
          if n.target == "conv1":
              assert n.type == TensorType(
                  (
                      4,
                      6,
                      sympy.floor(sympy.symbols("~0") - 4),
                      sympy.floor(sympy.symbols("~1") - 4),
                  )
              )
  
          elif n.target == "conv2":
              assert n.type == TensorType(
                  (
                      4,
                      16,
                      sympy.floor(sympy.symbols("~4") - 4),
                      sympy.floor(sympy.symbols("~5") - 4),
                  )
              )
  
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 31 for calling the TypeCheckerTest obj's underlying member method code with example usage: obj.test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten() and returns: out