code: def get_dummy_inputs(self, device, seed=0):
      input_image = floats_tensor(
          (1, 3, 32, 32), rng=random.Random(seed)
      ).to(device)
  
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
      inputs = {
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 1,
          "frame_size": 32,
          "output_type": "latent",
      }
      return inputs
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
  from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImg2ImgPipeline
  from diffusers.pipelines.shap_e import ShapERenderer
  from diffusers.utils.testing_utils import floats_tensor, load_image, load_numpy, nightly, require_torch_gpu, torch_device
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 10 for calling the ShapEImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs