code: def get_dummy_inputs(self, device, seed=0):
      generator_device = (
          "cpu" if not device.startswith("cuda") else "cuda"
      )
      if not str(device).startswith("mps"):
          generator = torch.Generator(
              device=generator_device
          ).manual_seed(seed)
      else:
          generator = torch.manual_seed(seed)
  
      inputs = {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel, logging
  from diffusers.utils.testing_utils import CaptureLogger, backend_empty_cache, enable_full_determinism, load_numpy, nightly, numpy_cosine_similarity_distance, require_torch_accelerator, require_torch_gpu, skip_mps, slow, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 2 for calling the StableDiffusion2PipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs