code: def dummy_model(self):
      def model(sample, t, *args):
          # if t is a tensor, match the number of dimensions of sample
          if isinstance(t, torch.Tensor):
              num_dims = len(sample.shape)
              # pad t with 1s to match num_dims
              t = (
                  t.reshape(-1, *(1,) * (num_dims - 1))
                  .to(sample.device)
                  .to(sample.dtype)
              )
  
          return sample * t / (t + 1)
  
      return model
  
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 5 for calling the SchedulerCommonTest obj's underlying member method code with example usage: obj.dummy_model() and returns: model, unknown