code: def test_rnn_decomp_module(
      self, device, dtype, module_info, training
  ):
      module_cls = module_info.module_cls
      module_inputs = module_info.module_inputs_func(
          module_info,
          device=device,
          dtype=dtype,
          requires_grad=True,
          training=training,
      )
      for module_input in module_inputs:
          if module_input.forward_input is None:
              continue
          args, kwargs = (
              module_input.constructor_input.args,
              module_input.constructor_input.kwargs,
          )
          m = module_cls(*args, **kwargs)
          m.to(device).to(dtype)
  
          args, kwargs = (
              module_input.forward_input.args,
              module_input.forward_input.kwargs,
          )
          with self.DecompCrossRefMode(
              self, self.precision, self.rel_tol, dtype, run_all=True
          ), enable_python_dispatcher():
              decomp_out = m(*args, **kwargs)
  
          non_decomp_out = m(*args, **kwargs)
          # without this check, incorrect decomps at the python dispatcher level can still pass because
          # they're checking aten decomps at the torch_dispatch level
          self.assertEqual(decomp_out, non_decomp_out)
  
imports: from collections import defaultdict
  from torch import Tensor
  import torch.autograd
  from torch._decomp import core_aten_decompositions, decomposition_table
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
  from torch.utils import _pytree
  from torch.testing import make_tensor
  from torch.testing._internal.common_cuda import tf32_off
  from torch.testing._internal.common_utils import unMarkDynamoStrictTest
  from torch.testing._internal.common_utils import is_iterable_of_tensors, IS_WINDOWS, TestCase, skipIfCrossRef, suppress_warnings, TEST_WITH_ASAN, TEST_WITH_SLOW, run_tests, skipIfTorchDynamo
  from torch.testing._internal.common_modules import module_db, modules
  from torch.testing._internal.common_device_type import onlyNativeDeviceTypes, ops, instantiate_device_type_tests, onlyCPU, onlyCUDA
  from torch.testing._internal.common_methods_invocations import op_db, skip, skipOps, xfail
  from torch._dispatch.python import enable_python_dispatcher
  from torch._ops import DispatchKey
  import itertools
  import functools
  from functools import partial
  import unittest
label_desc: Use image node_id 7 for calling the TestDecomp obj's underlying member method code with example usage: obj.test_rnn_decomp_module(device, dtype, module_info, training) without return types