code: def test_typecheck_basicblock(self):
      class BasicBlock(torch.nn.Module):
          expansion = 1
  
          def __init__(
              self,
              inplanes,
              planes,
              stride=1,
              downsample=None,
              groups=1,
              base_width=64,
              dilation=1,
          ):
              super().__init__()
              norm_layer = torch.nn.BatchNorm2d
              if groups != 1 or base_width != 64:
                  raise ValueError(
                      "BasicBlock only supports groups=1 and base_width=64"
                  )
              if dilation > 1:
                  raise NotImplementedError(
                      "Dilation > 1 not supported in BasicBlock"
                  )
              # Both self.conv1 and self.downsample layers downsample the input when stride != 1
              self.conv1 = conv3x3(inplanes, planes, stride)
              self.bn1 = norm_layer(planes)
              self.relu = torch.nn.ReLU(inplace=True)
              self.conv2 = conv3x3(planes, planes)
              self.bn2 = norm_layer(planes)
              self.downsample = downsample
              self.stride = stride
  
          def forward(self, x: TensorType((2, 2, 4, 5))):
              identity = x
  
              out = self.conv1(x)
              out = self.bn1(out)
              out = self.relu(out)
  
              out = self.conv2(out)
              out = self.bn2(out)
  
              if self.downsample is not None:
                  identity = self.downsample(x)
  
              out += identity
              out = self.relu(out)
  
              return out
  
      B = BasicBlock(2, 2)
  
      ast_rewriter = RewritingTracer()
      graph = ast_rewriter.trace(B)
      traced = GraphModule(ast_rewriter.root, graph, "gm")
  
      tc = GraphTypeChecker({}, traced)
      tc.type_check()
  
      for n in traced.graph.nodes:
          if n.target == "output":
              assert isinstance(n.type, TensorType)
              assert (
                  torch.Size(n.type.__args__)
                  == B.forward(torch.rand(2, 2, 4, 5)).size()
              )
  
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 18 for calling the TypeCheckerTest obj's underlying member method code with example usage: obj.test_typecheck_basicblock() and returns: out