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",
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }
  
      return inputs
  
imports: import gc
  import random
  import unittest
  import torch
  from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline
  from diffusers.models.attention_processor import AttnAddedKVProcessor
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
  from .None import IFPipelineTesterMixin
label_desc: Use image node_id 2 for calling the IFPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs