code: def get_dummy_inputs(self, device, seed=0, img_res=64):
      if str(device).startswith("mps"):
          generator = torch.manual_seed(seed)
      else:
          generator = torch.Generator(device=device).manual_seed(seed)
  
      # Get random floats in [0, 1] as image
      image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(
          device
      )
      image = image.cpu().permute(0, 2, 3, 1)[0]
      mask_image = torch.ones_like(image)
      controlnet_embedder_scale_factor = 2
      control_image = (
          floats_tensor(
              (
                  1,
                  3,
                  32 * controlnet_embedder_scale_factor,
                  32 * controlnet_embedder_scale_factor,
              ),
              rng=random.Random(seed),
          )
          .to(device)
          .cpu()
      )
      control_image = control_image.cpu().permute(0, 2, 3, 1)[0]
      # Convert image and mask_image to [0, 255]
      image = 255 * image
      mask_image = 255 * mask_image
      control_image = 255 * control_image
      # Convert to PIL image
      init_image = (
          Image.fromarray(np.uint8(image))
          .convert("RGB")
          .resize((img_res, img_res))
      )
      mask_image = (
          Image.fromarray(np.uint8(mask_image))
          .convert("L")
          .resize((img_res, img_res))
      )
      control_image = (
          Image.fromarray(np.uint8(control_image))
          .convert("RGB")
          .resize((img_res, img_res))
      )
  
      inputs = {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
          "image": init_image,
          "mask_image": mask_image,
          "control_image": control_image,
      }
      return inputs
  
imports: import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, EulerDiscreteScheduler, StableDiffusionXLControlNetInpaintPipeline, UNet2DConditionModel
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
  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 ControlNetPipelineSDXLFastTests obj's underlying member method code with example usage: obj.get_dummy_inputs(device, seed, img_res) and returns: inputs