code: def test_inference(self, model_arch: str):
      model_args = {"test_name": model_arch, "model_arch": model_arch}
      self._setup(model_args)
      pipeline = self.ORTMODEL_CLASS.from_pretrained(
          self.onnx_model_dirs[model_arch]
      )
  
      height, width = 128, 128
      inputs = self.generate_inputs(height=height, width=width)
      inputs["image"] = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo.png"
      ).resize((width, height))
      output = pipeline(
          **inputs, generator=np.random.RandomState(0)
      ).images[0, -3:, -3:, -1]
      expected_slice = np.array(
          [
              0.6515,
              0.5405,
              0.4858,
              0.5632,
              0.5174,
              0.5681,
              0.4948,
              0.4253,
              0.5080,
          ]
      )
  
      self.assertTrue(
          np.allclose(output.flatten(), expected_slice, atol=1e-1)
      )
  
imports: import random
  import unittest
  from typing import Dict
  import numpy
  import PIL
  import pytest
  import torch
  from diffusers import OnnxStableDiffusionImg2ImgPipeline, StableDiffusionPipeline, StableDiffusionXLPipeline
  from diffusers.utils import load_image
  from diffusers.utils.testing_utils import floats_tensor
  from packaging.version import Version, parse
  from parameterized import parameterized
  from transformers.testing_utils import require_torch_gpu
  from utils_onnxruntime_tests import MODEL_NAMES, SEED, ORTModelTestMixin
  from optimum.onnxruntime import ORTLatentConsistencyModelPipeline, ORTStableDiffusionImg2ImgPipeline, ORTStableDiffusionInpaintPipeline, ORTStableDiffusionPipeline, ORTStableDiffusionXLImg2ImgPipeline, ORTStableDiffusionXLPipeline
  from optimum.onnxruntime.modeling_diffusion import ORTModelTextEncoder, ORTModelUnet, ORTModelVaeDecoder, ORTModelVaeEncoder
  from optimum.pipelines.diffusers.pipeline_utils import VaeImageProcessor
  from optimum.utils.import_utils import _diffusers_version
  from optimum.utils.testing_utils import grid_parameters, require_diffusers, require_ort_rocm
label_desc: Use image node_id 1 for calling the ORTStableDiffusionXLImg2ImgPipelineTest obj's underlying member method code with example usage: obj.test_inference(model_arch) without return types