code: def get_dummy_inputs(self, device, seed=0):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(
          device
      )
      image = image.cpu().permute(0, 2, 3, 1)[0]
      init_image = (
          Image.fromarray(np.uint8(image))
          .convert("RGB")
          .resize((64, 64))
      )
      mask_image = (
          Image.fromarray(np.uint8(image + 4))
          .convert("RGB")
          .resize((64, 64))
      )
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
      inputs = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": init_image,
          "mask_image": mask_image,
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, require_torch_gpu, slow, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 2 for calling the StableDiffusion2InpaintPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs