code: def test_shape(self, model_arch: str):
      model_args = {"test_name": model_arch, "model_arch": model_arch}
      self._setup(model_args)
      height, width, batch_size = 128, 64, 1
      pipeline = self.ORTMODEL_CLASS.from_pretrained(
          self.onnx_model_dirs[model_arch]
      )
  
      if self.TASK == "image-to-image":
          input_types = ["np", "pil", "pt"]
      elif self.TASK == "text-to-image":
          input_types = ["np"]
      else:
          input_types = ["pil"]
  
      for input_type in input_types:
          if self.TASK == "image-to-image":
              inputs = self.generate_inputs(
                  height=height,
                  width=width,
                  batch_size=batch_size,
                  input_type=input_type,
              )
          else:
              inputs = self.generate_inputs(
                  height=height, width=width, batch_size=batch_size
              )
          for output_type in ["np", "pil", "latent"]:
              inputs["output_type"] = output_type
              outputs = pipeline(**inputs).images
              if output_type == "pil":
                  self.assertEqual(
                      (
                          len(outputs),
                          outputs[0].height,
                          outputs[0].width,
                      ),
                      (batch_size, height, width),
                  )
              elif output_type == "np":
                  self.assertEqual(
                      outputs.shape, (batch_size, height, width, 3)
                  )
              else:
                  self.assertEqual(
                      outputs.shape,
                      (
                          batch_size,
                          4,
                          height // pipeline.vae_scale_factor,
                          width // pipeline.vae_scale_factor,
                      ),
                  )
  
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 6 for calling the ORTStableDiffusionPipelineBase obj's underlying member method code with example usage: obj.test_shape(model_arch) without return types