code: def dummy_sample_deter(self):
      batch_size = 4
      num_channels = 3
      width = 8
  
      num_elems = batch_size * num_channels * width
      sample = torch.arange(num_elems)
      sample = sample.reshape(num_channels, width, batch_size)
      sample = sample / num_elems
      sample = sample.permute(2, 0, 1)
  
      return sample
  
imports: import tempfile
  import torch
  from diffusers import DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler
  from .test_schedulers import SchedulerCommonTest
label_desc: Use image node_id 3 for calling the UniPCMultistepScheduler1DTest obj's underlying member method code with example usage: obj.dummy_sample_deter() and returns: sample