code: def test_inference_ddim(self):
      ddim_scheduler = DDIMScheduler.from_pretrained(
          "runwayml/stable-diffusion-v1-5",
          subfolder="scheduler",
          revision="onnx",
      )
      sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
          "runwayml/stable-diffusion-v1-5",
          revision="onnx",
          scheduler=ddim_scheduler,
          safety_checker=None,
          feature_extractor=None,
          provider=self.gpu_provider,
          sess_options=self.gpu_options,
      )
      sd_pipe.set_progress_bar_config(disable=None)
  
      prompt = "open neural network exchange"
      generator = np.random.RandomState(0)
      output = sd_pipe(
          [prompt],
          guidance_scale=7.5,
          num_inference_steps=10,
          generator=generator,
          output_type="np",
      )
      image = output.images
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 512, 512, 3)
      expected_slice = np.array(
          [
              0.2867,
              0.1974,
              0.1481,
              0.7294,
              0.7251,
              0.6667,
              0.4194,
              0.5642,
              0.6486,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
  
imports: import tempfile
  import unittest
  import numpy
  from diffusers import DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionPipeline, PNDMScheduler
  from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
  from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
label_desc: Use image node_id 4 for calling the OnnxStableDiffusionPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_inference_ddim() without return types