code: def test_inference_default_pndm(self):
      # using the PNDM scheduler by default
      sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4",
          revision="onnx",
          safety_checker=None,
          feature_extractor=None,
          provider=self.gpu_provider,
          sess_options=self.gpu_options,
      )
      sd_pipe.set_progress_bar_config(disable=None)
  
      prompt = "A painting of a squirrel eating a burger"
      np.random.seed(0)
      output = sd_pipe(
          [prompt],
          guidance_scale=6.0,
          num_inference_steps=10,
          output_type="np",
      )
      image = output.images
  
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 512, 512, 3)
      expected_slice = np.array(
          [
              0.0452,
              0.0390,
              0.0087,
              0.0350,
              0.0617,
              0.0364,
              0.0544,
              0.0523,
              0.0720,
          ]
      )
  
      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 3 for calling the OnnxStableDiffusionPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_inference_default_pndm() without return types