code: def get_dummy_inputs(self, device, seed=0, pil_image=True):
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
  
      input_image = floats_tensor(
          (1, 3, 32, 32), rng=random.Random(seed)
      ).to(device)
  
      if pil_image:
          input_image = input_image * 0.5 + 0.5
          input_image = input_image.clamp(0, 1)
          input_image = (
              input_image.cpu().permute(0, 2, 3, 1).float().numpy()
          )
          input_image = DiffusionPipeline.numpy_to_pil(input_image)[0]
  
      return {
          "prompt": "An anime racoon running a marathon",
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "np",
      }
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPImageProcessor, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection
  from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImg2ImgPipeline, UNet2DConditionModel
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, nightly, require_torch_gpu, skip_mps, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 2 for calling the StableUnCLIPImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed, pil_image) and returns: dict