code: def test_full_loop_with_noise(self):
      scheduler_class = self.scheduler_classes[0]
      scheduler_config = self.get_scheduler_config()
      scheduler = scheduler_class(**scheduler_config)
  
      num_inference_steps = 10
      t_start = 8
  
      model = self.dummy_model()
      sample = self.dummy_sample_deter
      scheduler.set_timesteps(num_inference_steps)
  
      # add noise
      noise = self.dummy_noise_deter
      timesteps = scheduler.timesteps[t_start * scheduler.order :]
      sample = scheduler.add_noise(sample, noise, timesteps[:1])
  
      for i, t in enumerate(timesteps):
          residual = model(sample, t)
          sample = scheduler.step(residual, t, sample).prev_sample
  
      result_sum = torch.sum(torch.abs(sample))
      result_mean = torch.mean(torch.abs(sample))
  
      assert (
          abs(result_sum.item() - 315.5757) < 1e-2
      ), f" expected result sum 315.5757, but get {result_sum}"
      assert (
          abs(result_mean.item() - 0.4109) < 1e-3
      ), f" expected result mean 0.4109, but get {result_mean}"
  
imports: import tempfile
  import torch
  from diffusers import DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler
  from .test_schedulers import SchedulerCommonTest
label_desc: Use image node_id 18 for calling the UniPCMultistepSchedulerTest obj's underlying member method code with example usage: obj.test_full_loop_with_noise() without return types