code: def test_type_check_conv2D_2_fully_static(self):
      annotation_list = [
          (1, 2, 3, 5),
          (2, 5, 6, 9),
          (10, 15, 13, 14),
          (10, Dyn, 13, 14),
          (Dyn, Dyn, Dyn, 3),
      ]
      input_list = [
          (1, 2, 3, 5),
          (2, 5, 6, 9),
          (10, 15, 13, 14),
          (10, 15, 13, 14),
          (1, 2, 2, 3),
      ]
      intermediate_types = [
          (1, Dyn, Dyn, 7),
          (2, Dyn, 4, 6),
          (10, 15, Dyn, 5),
          (10, 15, 7, 7),
          (1, Dyn, Dyn, Dyn),
      ]
      in_planes_list = [2, 5, 15, 15, 2]
      stride_list = [1, 2, 3, 2, 2]
      out_planes_list = [2, 5, 15, 15, 2]
      groups_list = [1, 5, 5, 5, 2]
      dilation_list = [1, 2, 3, 3, 3]
      padding_list = [1, 2, 3, 3, 3]
      kernel_size_list = [1, 2, 3, 3, 3]
      output_types = [
          (1, 2, Dyn, 7),
          (2, 5, 4, 6),
          (10, 15, Dyn, 5),
          (10, 15, 7, 7),
          (1, 2, Dyn, Dyn),
      ]
  
      for i in range(5):
          annotation = annotation_list[i]
          input = input_list[i]
          in_planes = in_planes_list[i]
          stride = stride_list[i]
          out_planes = out_planes_list[i]
          groups = groups_list[i]
          dilation = dilation_list[i]
          padding = padding_list[i]
          kernel_size = kernel_size_list[i]
          intermediate_type = intermediate_types[i]
  
          class BasicBlock(torch.nn.Module):
              def __init__(
                  self,
                  in_planes,
                  out_planes,
                  kernel_size,
                  stride,
                  padding,
                  groups,
                  dilation,
              ):
                  super().__init__()
                  self.conv1 = torch.nn.Conv2d(
                      in_channels=in_planes,
                      out_channels=out_planes,
                      kernel_size=kernel_size,
                      stride=stride,
                      padding=padding,
                      groups=groups,
                      bias=False,
                      dilation=dilation,
                  )
  
              def forward(self, x):
                  out = self.conv1(x)
                  return out
  
          B = BasicBlock(
              in_planes,
              out_planes,
              kernel_size,
              stride,
              padding,
              groups,
              dilation,
          )
          ast_rewriter = RewritingTracer()
          graph = ast_rewriter.trace(B)
          traced = GraphModule(ast_rewriter.root, graph, "gm")
  
          # annotate our argument
          for n in graph.nodes:
              if n.op == "placeholder":
                  n.type = TensorType(annotation)
  
          b = B.forward(torch.rand(input))
          tc = GraphTypeChecker({}, traced)
          tc.type_check()
  
          for n in graph.nodes:
              if n.op == "output":
                  assert is_consistent(n.type, TensorType(b.size()))
  
          # test with intermediate annotations
          class BasicBlock(torch.nn.Module):
              def __init__(
                  self,
                  in_planes,
                  out_planes,
                  kernel_size,
                  stride,
                  padding,
                  groups,
                  dilation,
              ):
                  super().__init__()
                  self.conv1 = torch.nn.Conv2d(
                      in_channels=in_planes,
                      out_channels=out_planes,
                      kernel_size=kernel_size,
                      stride=stride,
                      padding=padding,
                      groups=groups,
                      bias=False,
                      dilation=dilation,
                  )
  
              def forward(self, x):
                  out = self.conv1(x)
                  return out
  
          B = BasicBlock(
              in_planes,
              out_planes,
              kernel_size,
              stride,
              padding,
              groups,
              dilation,
          )
          ast_rewriter = RewritingTracer()
          graph = ast_rewriter.trace(B)
          traced = GraphModule(ast_rewriter.root, graph, "gm")
  
          # populate our intermediate notes
          for n in traced.graph.nodes:
              if n.op == "call_module":
                  n.type = TensorType(intermediate_type)
  
          tc = GraphTypeChecker({}, traced)
          tc.type_check()
  
          for n in traced.graph.nodes:
              if n.op == "output":
                  assert n.type == TensorType(output_types[i])
                  assert is_consistent(n.type, TensorType(b.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 17 for calling the TypeCheckerTest obj's underlying member method code with example usage: obj.test_type_check_conv2D_2_fully_static() and returns: out, out