code: def test_step_shape(self):
      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)
  
          sample = self.dummy_sample
          residual = 0.1 * sample
  
          if num_inference_steps is not None and hasattr(
              scheduler, "set_timesteps"
          ):
              scheduler.set_timesteps(num_inference_steps)
          elif num_inference_steps is not None and not hasattr(
              scheduler, "set_timesteps"
          ):
              kwargs["num_inference_steps"] = num_inference_steps
  
          # copy over dummy past residuals (must be done after set_timesteps)
          dummy_past_residuals = [
              residual + 0.2,
              residual + 0.15,
              residual + 0.10,
          ]
          scheduler.model_outputs = dummy_past_residuals[
              : scheduler.config.solver_order
          ]
  
          time_step_0 = scheduler.timesteps[5]
          time_step_1 = scheduler.timesteps[6]
  
          output_0 = scheduler.step(
              residual, time_step_0, sample, **kwargs
          ).prev_sample
          output_1 = scheduler.step(
              residual, time_step_1, sample, **kwargs
          ).prev_sample
  
          self.assertEqual(output_0.shape, sample.shape)
          self.assertEqual(output_0.shape, output_1.shape)
  
imports: import tempfile
  import torch
  from diffusers import DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler
  from .test_schedulers import SchedulerCommonTest
label_desc: Use image node_id 6 for calling the DPMSolverMultistepSchedulerTest obj's underlying member method code with example usage: obj.test_step_shape() without return types