code: def test_outputs_equivalence(self):
      def set_nan_tensor_to_zero(t):
          # Temporary fallback until `aten::_index_put_impl_` is implemented in mps
          # Track progress in https://github.com/pytorch/pytorch/issues/77764
          device = t.device
          if device.type == "mps":
              t = t.to("cpu")
          t[t != t] = 0
          return t.to(device)
  
      def recursive_check(tuple_object, dict_object):
          if isinstance(tuple_object, (List, Tuple)):
              for tuple_iterable_value, dict_iterable_value in zip(
                  tuple_object, dict_object.values()
              ):
                  recursive_check(
                      tuple_iterable_value, dict_iterable_value
                  )
          elif isinstance(tuple_object, Dict):
              for tuple_iterable_value, dict_iterable_value in zip(
                  tuple_object.values(), dict_object.values()
              ):
                  recursive_check(
                      tuple_iterable_value, dict_iterable_value
                  )
          elif tuple_object is None:
              return
          else:
              self.assertTrue(
                  torch.allclose(
                      set_nan_tensor_to_zero(tuple_object),
                      set_nan_tensor_to_zero(dict_object),
                      atol=1e-5,
                  ),
                  msg=(
                      "Tuple and dict output are not equal. Difference:"
                      f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                      f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                      f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                  ),
              )
  
      (
          init_dict,
          inputs_dict,
      ) = self.prepare_init_args_and_inputs_for_common()
  
      model = self.model_class(**init_dict)
      model.to(torch_device)
      model.eval()
  
      with torch.no_grad():
          outputs_dict = model(**inputs_dict)
          outputs_tuple = model(**inputs_dict, return_dict=False)
  
      recursive_check(outputs_tuple, outputs_dict)
  
imports: import inspect
  import tempfile
  import traceback
  import unittest
  import unittest.mock
  import uuid
  from typing import Dict, List, Tuple
  import numpy
  import requests_mock
  import torch
  from huggingface_hub import delete_repo
  from requests.exceptions import HTTPError
  from diffusers.models import UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor, AttnProcessor2_0, XFormersAttnProcessor
  from diffusers.training_utils import EMAModel
  from diffusers.utils import is_xformers_available, logging
  from diffusers.utils.testing_utils import CaptureLogger, require_python39_or_higher, require_torch_2, require_torch_gpu, run_test_in_subprocess, torch_device
  from ..others.test_utils import TOKEN, USER, is_staging_test
label_desc: Use image node_id 13 for calling the ModelTesterMixin obj's underlying member method code with example usage: obj.test_outputs_equivalence() and returns: t