code: def generate_inputs(self, height=128, width=128, batch_size=1):
      inputs = super(
          ORTStableDiffusionInpaintPipelineTest, self
      ).generate_inputs(height, width)
      inputs["image"] = _create_image(
          height=height, width=width, batch_size=1, input_type="pil"
      )[0]
      inputs["mask_image"] = _create_image(
          height=height, width=width, batch_size=1, input_type="pil"
      )[0]
      return inputs
  
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 ORTStableDiffusionInpaintPipelineTest obj's underlying member method code with example usage: obj.generate_inputs(height, width, batch_size) and returns: inputs