code: def get_dummy_inputs(self, device, seed=0):
      image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))
      image = image.cpu().permute(0, 2, 3, 1)[0]
      image = (
          Image.fromarray(np.uint8(image))
          .convert("RGB")
          .resize((32, 32))
      )
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
      inputs = {
          "image": 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 CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
  from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PNDMScheduler, StableDiffusionImageVariationPipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, nightly, numpy_cosine_similarity_distance, print_tensor_test, require_torch_gpu, slow, torch_device
  from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 2 for calling the StableDiffusionImageVariationPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs