code: def test_full_uneven_loop(self):
      scheduler = DPMSolverSinglestepScheduler(
          **self.get_scheduler_config()
      )
      num_inference_steps = 50
      model = self.dummy_model()
      sample = self.dummy_sample_deter
      scheduler.set_timesteps(num_inference_steps)
  
      # make sure that the first t is uneven
      for i, t in enumerate(scheduler.timesteps[3:]):
          residual = model(sample, t)
          sample = scheduler.step(residual, t, sample).prev_sample
  
      result_mean = torch.mean(torch.abs(sample))
  
      assert abs(result_mean.item() - 0.2574) < 1e-3
  
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 DPMSolverSinglestepSchedulerTest obj's underlying member method code with example usage: obj.test_full_uneven_loop() without return types