code: def test_tuple_specialization(self):
      @torch.jit.script
      def f(t, s):
          # type: (Tuple[Tensor, Tuple[int, Tensor]], str) -> Tensor
          x, t2 = t
          _, y = t2
          return x + y
  
      t = (
          torch.randn(2, 2),
          (1, torch.randn(2, 2)),
      )
      f(t, "hi")
      graph = f.graph_for(t, "hi")
      input_types = list(next(graph.inputs()).type().elements())
      w = input_types[0]
      self.assertEqual(input_types[0].kind(), "TensorType")
      self.assertEqual(
          input_types[1].elements()[1].kind(), "TensorType"
      )
  
imports: import os
  import sys
  import torch
  from torch.testing._internal.jit_utils import JitTestCase, make_global
  from torch.testing._internal.common_utils import IS_WINDOWS
  from collections import namedtuple
  from typing import List, Tuple, Dict, NamedTuple
label_desc: Use image node_id 6 for calling the TestTyping obj's underlying member method code with example usage: obj.test_tuple_specialization() and returns: unknown