code: def test_full_loop_no_noise(self):
      scheduler_class = self.scheduler_classes[0]
      scheduler_config = self.get_scheduler_config()
      scheduler = scheduler_class(**scheduler_config)
  
      num_trained_timesteps = len(scheduler)
  
      model = self.dummy_model()
      sample = self.dummy_sample_deter
      generator = torch.manual_seed(0)
  
      for t in reversed(range(num_trained_timesteps)):
          # 1. predict noise residual
          residual = model(sample, t)
  
          # 2. predict previous mean of sample x_t-1
          pred_prev_sample = scheduler.step(
              residual, t, sample, generator=generator
          ).prev_sample
  
          sample = pred_prev_sample
  
      result_sum = torch.sum(torch.abs(sample))
      result_mean = torch.mean(torch.abs(sample))
  
      assert abs(result_sum.item() - 258.9606) < 1e-2
      assert abs(result_mean.item() - 0.3372) < 1e-3
  
imports: import torch
  from diffusers import DDPMParallelScheduler
  from .test_schedulers import SchedulerCommonTest
label_desc: Use image node_id 13 for calling the DDPMParallelSchedulerTest obj's underlying member method code with example usage: obj.test_full_loop_no_noise() without return types