code: def test_attributes(self):
      @torch.jit.script
      class Inner2:
          def __init__(self):
              self.b = "a string"
  
      @torch.jit.script
      class Foo:
          def __init__(self):
              self.a = 4
              self.inner = Inner2()
  
      @torch.jit.script
      class SFoo:
          def __init__(self):
              self.a = 4
              self.inner = Inner2()
  
          def __setstate__(self, obj: Tuple[int, Inner2]) -> None:
              a, inner = obj
              self.a = a
              self.inner = inner
  
          def __getstate__(self):
              return (self.a, self.inner)
  
      untyped_values = (
          ("my_dict", {"I": "am", "a test": "test"}),
          ("my_float", 2.3),
          ("my_int", 99),
          ("my_bool", False),
          ("my_tuple", (1, 2, 3, 4)),
          ("my_list", [(1, 2), (3, 4)]),
          # ('my_tensor', torch.randn(2, 2)),
          ("my_int_list", [1, 2, 3, 4]),
          # ('my_tensor_list', [torch.ones(2, 2) + i for i in range(4)]),
          ("my_bool_list", [True, True, False, True]),
          ("my_float_list", [1.0, 2.0, 3.0, 4.0]),
          ("my_str_list", ["hello", "bye"]),
      )
      typed_values = (
          ("my_empty_list", []),
          ("my_empty_dict", {}),
          ("my_none", None),
          ("my_object", Foo()),
          ("my_object2", SFoo()),
      )
  
      class M(torch.nn.Module):
          # TODO: re-enable this once this test is in a Python 3-only syntax
          # file
          # my_empty_list : List[int]
          # my_empty_dict : Dict[str, int]
          # my_none : Optional[int]
  
          def forward(self, x):
              return (
                  self.my_dict,
                  self.my_float,
                  self.my_int,
                  self.my_bool,
                  # self.my_tensor,
                  self.my_int_list,
                  # self.my_tensor_list,
                  self.my_bool_list,
                  self.my_float_list,
                  self.my_str_list,
                  self.my_empty_list,
                  self.my_empty_dict,
                  self.my_none,
                  self.my_object.a,
                  self.my_object.inner.b,
                  self.my_object.a,
                  self.my_object2.inner.b,
              )
  
      # TODO: as a followup, fix this test
      # We can't define class attributes like we should be doing:
      #   class M(torch.nn.Module):
      #       my_empty_list : List[int]
      #       my_empty_dict : Dict[str, int]
      #       my_none : Optional[int]
      #       my_out_of_line_attribute: List[int] = [1, 2, 3]
      # since there's no string frontend for Python classes (so the `define`)
      # trick doesn't work.
      M.__annotations__ = {
          "my_empty_list": List[int],
          "my_empty_dict": Dict[str, int],
          "my_none": Optional[int],
          "my_object": Foo,
          "my_object2": SFoo,
      }
  
      m = M()
      for name, value in untyped_values + typed_values:
          setattr(m, name, value)
  
      self.checkModule(m, (torch.randn(5, 5),))
  
imports: import os
  import re
  import sys
  import types
  import typing
  import typing_extensions
  from typing import List, Dict, Optional, Tuple
  import torch
  import torch.jit.frontend
  import torch.nn
  from torch import Tensor
  from torch.testing import FileCheck
  from collections import OrderedDict
  from torch.testing._internal.jit_utils import JitTestCase, _tmp_donotuse_dont_inline_everything
label_desc: Use image node_id 27 for calling the TestRecursiveScript obj's underlying member method code with example usage: obj.test_attributes() and returns: self,  self, self,  self,  self,  self,  self,  self,  self,  self,  self,  self,  self,  self,  self,  self,  self