code: def _create_image(
      height=128, width=128, batch_size=1, channel=3, input_type="pil"
  ):
      if input_type == "pil":
          image = load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"
          ).resize((width, height))
      elif input_type == "np":
          image = np.random.rand(height, width, channel)
      elif input_type == "pt":
          image = torch.rand((channel, height, width))
  
      return [image] * batch_size
  
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 a global function with example usage: _create_image(height, width, batch_size, channel, input_type) and returns: unknown