code: def test_pipeline_on_rocm_ep(
      self, test_name: str, model_arch: str, provider: str
  ):
      model_args = {"test_name": test_name, "model_arch": model_arch}
      self._setup(model_args)
      pipeline = self.ORTMODEL_CLASS.from_pretrained(
          self.onnx_model_dirs[test_name], provider=provider
      )
      height, width, batch_size = 32, 64, 1
      inputs = self.generate_inputs(
          height=height, width=width, batch_size=batch_size
      )
      outputs = pipeline(**inputs).images
      # Verify model devices
      self.assertEqual(pipeline.device.type.lower(), "cuda")
      # Verify model outptus
      self.assertIsInstance(outputs, np.ndarray)
      self.assertEqual(outputs.shape, (batch_size, height, width, 3))
  
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 4 for calling the ORTStableDiffusionPipelineBase obj's underlying member method code with example usage: obj.test_pipeline_on_rocm_ep(test_name, model_arch, provider) without return types