code: def test_pipeline_dpm_multistep(self):
      pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(
          self.hub_checkpoint, provider="CPUExecutionProvider"
      )
      pipe.scheduler = DPMSolverMultistepScheduler.from_config(
          pipe.scheduler.config
      )
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs()
      image = pipe(**inputs).images
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 512, 512, 3)
      expected_slice = np.array(
          [
              0.7659278,
              0.76437664,
              0.75579107,
              0.7691116,
              0.77666986,
              0.7727672,
              0.7758664,
              0.7812226,
              0.76942515,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
  
imports: import random
  import unittest
  import numpy
  from diffusers import DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionUpscalePipeline, PNDMScheduler
  from diffusers.utils.testing_utils import floats_tensor, is_onnx_available, load_image, nightly, require_onnxruntime, require_torch_gpu
  from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
label_desc: Use image node_id 4 for calling the OnnxStableDiffusionUpscalePipelineFastTests obj's underlying member method code with example usage: obj.test_pipeline_dpm_multistep() without return types