code: def test_consistency_model_pipeline_multistep(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      pipe = ConsistencyModelPipeline(**components)
      pipe = pipe.to(device)
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      image = pipe(**inputs).images
      assert image.shape == (1, 32, 32, 3)
  
      image_slice = image[0, -3:, -3:, -1]
      expected_slice = np.array(
          [
              0.3572,
              0.6273,
              0.4031,
              0.3961,
              0.4321,
              0.5730,
              0.5266,
              0.4780,
              0.5004,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
  
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 5 for calling the ConsistencyModelPipelineFastTests obj's underlying member method code with example usage: obj.test_consistency_model_pipeline_multistep() without return types