code: def get_dummy_inputs(self, device, seed=0):
      image_embeds = floats_tensor(
          (1, self.text_embedder_hidden_size), rng=random.Random(seed)
      ).to(device)
      negative_image_embeds = floats_tensor(
          (1, self.text_embedder_hidden_size),
          rng=random.Random(seed + 1),
      ).to(device)
      # create init_image
      image = floats_tensor((1, 3, 64, 64), 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((256, 256))
      )
  
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
      inputs = {
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 64,
          "width": 64,
          "num_inference_steps": 10,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from diffusers import DDIMScheduler, KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline, UNet2DConditionModel, VQModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, require_torch_gpu, slow, torch_device
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 10 for calling the Dummies obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs