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)
  
      controlnet_embedder_scale_factor = 2
      image = randn_tensor(
          (
              1,
              3,
              32 * controlnet_embedder_scale_factor,
              32 * controlnet_embedder_scale_factor,
          ),
          generator=generator,
          device=torch.device(device),
      )
  
      inputs = {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
          "image": image,
      }
  
      return inputs
  
imports: import gc
  import tempfile
  import traceback
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, DDIMScheduler, EulerDiscreteScheduler, StableDiffusionControlNetPipeline, UNet2DConditionModel
  from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, load_image, load_numpy, require_python39_or_higher, require_torch_2, require_torch_gpu, run_test_in_subprocess, slow, torch_device
  from diffusers.utils.torch_utils import randn_tensor
  from ..pipeline_params import IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_BATCH_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 ControlNetPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed) and returns: inputs