code: def test_callback(self, model_arch: str):
      def callback_fn(
          step: int, timestep: int, latents: np.ndarray
      ) -> None:
          callback_fn.has_been_called = True
          callback_fn.number_of_steps += 1
  
      pipe = self.ORTMODEL_CLASS.from_pretrained(
          MODEL_NAMES[model_arch], export=True
      )
      callback_fn.has_been_called = False
      callback_fn.number_of_steps = 0
      inputs = self.generate_inputs(height=64, width=64)
      pipe(**inputs, callback=callback_fn, callback_steps=1)
      self.assertTrue(callback_fn.has_been_called)
      self.assertEqual(
          callback_fn.number_of_steps, inputs["num_inference_steps"]
      )
  
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 5 for calling the ORTStableDiffusionPipelineBase obj's underlying member method code with example usage: obj.test_callback(model_arch) without return types