code: def test_scheduler_outputs_equivalence(self):
      def set_nan_tensor_to_zero(t):
          return t.at[t != t].set(0)
  
      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(
                  jnp.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" {jnp.max(jnp.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                      f" {jnp.isnan(tuple_object).any()} and `inf`: {jnp.isinf(tuple_object)}. Dict has"
                      f" `nan`: {jnp.isnan(dict_object).any()} and `inf`: {jnp.isinf(dict_object)}."
                  ),
              )
  
      kwargs = dict(self.forward_default_kwargs)
      num_inference_steps = kwargs.pop("num_inference_steps", None)
  
      for scheduler_class in self.scheduler_classes:
          scheduler_config = self.get_scheduler_config()
          scheduler = scheduler_class(**scheduler_config)
          state = scheduler.create_state()
  
          sample, _ = self.dummy_sample
          residual = 0.1 * sample
  
          if num_inference_steps is not None and hasattr(
              scheduler, "set_timesteps"
          ):
              state = scheduler.set_timesteps(
                  state, num_inference_steps, shape=sample.shape
              )
          elif num_inference_steps is not None and not hasattr(
              scheduler, "set_timesteps"
          ):
              kwargs["num_inference_steps"] = num_inference_steps
  
          outputs_dict = scheduler.step(
              state, residual, 0, sample, **kwargs
          )
  
          if num_inference_steps is not None and hasattr(
              scheduler, "set_timesteps"
          ):
              state = scheduler.set_timesteps(
                  state, num_inference_steps, shape=sample.shape
              )
          elif num_inference_steps is not None and not hasattr(
              scheduler, "set_timesteps"
          ):
              kwargs["num_inference_steps"] = num_inference_steps
  
          outputs_tuple = scheduler.step(
              state, residual, 0, sample, return_dict=False, **kwargs
          )
  
          recursive_check(outputs_tuple[0], outputs_dict.prev_sample)
  
imports: import inspect
  import tempfile
  import unittest
  from typing import Dict, List, Tuple
  from diffusers import FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxPNDMScheduler
  from diffusers.utils import is_flax_available
  from diffusers.utils.testing_utils import require_flax
label_desc: Use image node_id 4 for calling the FlaxPNDMSchedulerTest obj's underlying member method code with example usage: obj.test_scheduler_outputs_equivalence() and returns: t