code: def test_flatten_fully_static(self):
      annotation_list = [
          Dyn,
          TensorType((2, 5, 6, 9)),
          TensorType((10, 15, 13, 14)),
          TensorType((10, Dyn, 13, 14)),
          TensorType((Dyn, Dyn, Dyn, 10)),
      ]
      input_list = [
          (1, 2, 3, 5),
          (2, 5, 6, 9),
          (10, 15, 13, 14),
          (10, 15, 13, 14),
          (2, 2, 10, 10),
      ]
  
      intermediate_list = [
          Dyn,
          (2, 5, 6, 9),
          (10, 15, 13, 14),
          (10, 15, 13, 14),
          (2, 2, 10, 10),
      ]
  
      start_dim = [1, 2, 1, 2, 0]
      end_dim = [1, 3, 3, 3, -2]
  
      for i in range(5):
          annotation = annotation_list[i]
          input = input_list[i]
          # intermediate_type = intermediate_list[i]
  
          class BasicBlock(torch.nn.Module):
              def __init__(self, start, end):
                  super().__init__()
                  self.start = start
                  self.end = end
  
              def forward(self, x):
                  out = torch.flatten(x, self.start, self.end)
                  return out
  
          B = BasicBlock(start_dim[i], end_dim[i])
          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 = 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()))
  
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 25 for calling the TypeCheckerTest obj's underlying member method code with example usage: obj.test_flatten_fully_static() and returns: out