code: def prepare_latents(
      self,
      image,
      timestep,
      batch_size,
      num_images_per_prompt,
      dtype,
      device,
      generator=None,
  ):
      if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
          )
  
      image = image.to(device=device, dtype=dtype)
  
      batch_size = batch_size * num_images_per_prompt
  
      if image.shape[1] == 4:
          init_latents = image
  
      else:
          _image = image.cpu().detach().numpy()
          init_latents = self.vae_encoder(sample=_image)[0]
          init_latents = torch.from_numpy(init_latents).to(
              device=device, dtype=dtype
          )
          init_latents = 0.18215 * init_latents
  
      if (
          batch_size > init_latents.shape[0]
          and batch_size % init_latents.shape[0] == 0
      ):
          # expand init_latents for batch_size
          deprecation_message = (
              f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
              " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
              " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
              " your script to pass as many initial images as text prompts to suppress this warning."
          )
          deprecate(
              "len(prompt) != len(image)",
              "1.0.0",
              deprecation_message,
              standard_warn=False,
          )
          additional_image_per_prompt = (
              batch_size // init_latents.shape[0]
          )
          init_latents = torch.cat(
              [init_latents] * additional_image_per_prompt, dim=0
          )
      elif (
          batch_size > init_latents.shape[0]
          and batch_size % init_latents.shape[0] != 0
      ):
          raise ValueError(
              f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
          )
      else:
          init_latents = torch.cat([init_latents], dim=0)
  
      shape = init_latents.shape
      noise = randn_tensor(
          shape, generator=generator, device=device, dtype=dtype
      )
  
      # get latents
      init_latents = self.scheduler.add_noise(
          init_latents, noise, timestep
      )
      latents = init_latents
  
      return latents
  
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 9 for calling the OnnxStableDiffusionControlNetImg2ImgPipeline obj's underlying member method code with example usage: obj.prepare_latents(image, timestep, batch_size, num_images_per_prompt, dtype, device, generator) and returns: latents