code: def test_vae_image_processor_np(self):
      image_processor = VaeImageProcessor(
          do_resize=False, do_normalize=True
      )
      input_np = np.stack(
          _create_image(height=8, width=8, input_type="np")
      )
      for output_type in ["np", "pil"]:
          out = image_processor.postprocess(
              image_processor.preprocess(input_np),
              output_type=output_type,
          )
          out_np = to_np(out)
          in_np = (
              (input_np * 255).round()
              if output_type == "pil"
              else input_np
          )
          self.assertTrue(np.allclose(in_np, out_np, atol=1e-6))
  
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 2 for calling the ImageProcessorTest obj's underlying member method code with example usage: obj.test_vae_image_processor_np() without return types