code: def test_consistency_model_cd_onestep(self):
      unet = UNet2DModel.from_pretrained(
          "diffusers/consistency_models",
          subfolder="diffusers_cd_imagenet64_l2",
      )
      scheduler = CMStochasticIterativeScheduler(
          num_train_timesteps=40,
          sigma_min=0.002,
          sigma_max=80.0,
      )
      pipe = ConsistencyModelPipeline(unet=unet, scheduler=scheduler)
      pipe.to(torch_device=torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_inputs()
      inputs["num_inference_steps"] = 1
      inputs["timesteps"] = None
      image = pipe(**inputs).images
      assert image.shape == (1, 64, 64, 3)
  
      image_slice = image[0, -3:, -3:, -1]
  
      expected_slice = np.array(
          [
              0.0059,
              0.0003,
              0.0000,
              0.0023,
              0.0052,
              0.0007,
              0.0165,
              0.0081,
              0.0095,
          ]
      )
  
      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 ConsistencyModelPipelineSlowTests obj's underlying member method code with example usage: obj.test_consistency_model_cd_onestep() without return types