code: def dummy_noise_deter(self):
      batch_size = 4
      num_channels = 3
      height = 8
      width = 8
  
      num_elems = batch_size * num_channels * height * width
      sample = torch.arange(num_elems).flip(-1)
      sample = sample.reshape(num_channels, height, width, batch_size)
      sample = sample / num_elems
      sample = sample.permute(3, 0, 1, 2)
  
      return sample
  
imports: import inspect
  import json
  import os
  import tempfile
  import unittest
  import uuid
  from typing import Dict, List, Tuple
  import numpy
  import torch
  from huggingface_hub import delete_repo
  import diffusers
  from diffusers import CMStochasticIterativeScheduler, DDIMScheduler, DEISMultistepScheduler, DiffusionPipeline, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, IPNDMScheduler, LMSDiscreteScheduler, UniPCMultistepScheduler, VQDiffusionScheduler, logging
  from diffusers.configuration_utils import ConfigMixin, register_to_config
  from diffusers.schedulers.scheduling_utils import SchedulerMixin
  from diffusers.utils.testing_utils import CaptureLogger, torch_device
  from ..others.test_utils import TOKEN, USER, is_staging_test
label_desc: Use image node_id 2 for calling the SchedulerCommonTest obj's underlying member method code with example usage: obj.dummy_noise_deter() and returns: sample