code: def dummy_sample(self):
      batch_size = 4
      num_channels = 3
      width = 8
  
      sample = torch.rand((batch_size, num_channels, width))
  
      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 1 for calling the UniPCMultistepScheduler1DTest obj's underlying member method code with example usage: obj.dummy_sample() and returns: sample