code: def prepare_control_image(
      self,
      image,
      width,
      height,
      batch_size,
      num_images_per_prompt,
      device,
      dtype,
      do_classifier_free_guidance=False,
      guess_mode=False,
  ):
      image = self.control_image_processor.preprocess(
          image, height=height, width=width
      ).to(dtype=torch.float32)
      image_batch_size = image.shape[0]
  
      if image_batch_size == 1:
          repeat_by = batch_size
      else:
          # image batch size is the same as prompt batch size
          repeat_by = num_images_per_prompt
  
      image = image.repeat_interleave(repeat_by, dim=0)
  
      image = image.to(device=device, dtype=dtype)
  
      if do_classifier_free_guidance and not guess_mode:
          image = torch.cat([image] * 2)
  
      return image
  
imports: import argparse
  import inspect
  import os
  import time
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from PIL import Image
  from transformers import CLIPTokenizer
  from diffusers import OnnxRuntimeModel, StableDiffusionImg2ImgPipeline, UniPCMultistepScheduler
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, logging, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 7 for calling the OnnxStableDiffusionControlNetImg2ImgPipeline obj's underlying member method code with example usage: obj.prepare_control_image(image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance, guess_mode) and returns: image