code: def get_dummy_inputs(self, device, seed=0):
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
      inputs = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": self.dummy_image.cpu(),
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  import diffusers
  from diffusers import AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, require_torch_gpu, slow, 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
label_desc: Use image node_id 3 for calling the StableDiffusionLatentUpscalePipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs