code: def get_inputs(
      self, device, generator_device="cpu", dtype=torch.float32, seed=0
  ):
      generator = torch.Generator(device=generator_device).manual_seed(
          seed
      )
      init_image = load_image(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
          "/stable_diffusion_imgvar/input_image_vermeer.png"
      )
      latents = np.random.RandomState(seed).standard_normal(
          (1, 4, 64, 64)
      )
      latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
      inputs = {
          "image": init_image,
          "latents": latents,
          "generator": generator,
          "num_inference_steps": 3,
          "guidance_scale": 7.5,
          "output_type": "np",
      }
      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, 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 StableDiffusionImageVariationPipelineSlowTests obj's underlying member method code with example usage: obj.get_inputs(device, generator_device, dtype, seed) and returns: inputs