code: def get_dummy_components(self, class_cond=False):
      if class_cond:
          unet = self.dummy_cond_unet
      else:
          unet = self.dummy_uncond_unet
  
      # Default to CM multistep sampler
      scheduler = CMStochasticIterativeScheduler(
          num_train_timesteps=40,
          sigma_min=0.002,
          sigma_max=80.0,
      )
  
      components = {
          "unet": unet,
          "scheduler": scheduler,
      }
  
      return components
  
imports: import gc
  import unittest
  import numpy
  import torch
  from torch.backends.cuda import sdp_kernel
  from diffusers import CMStochasticIterativeScheduler, ConsistencyModelPipeline, UNet2DModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_2, require_torch_gpu, torch_device
  from diffusers.utils.torch_utils import randn_tensor
  from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the ConsistencyModelPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_components(class_cond) and returns: components