code: def prepare_latents(
      self,
      image,
      timestep,
      batch_size,
      num_images_per_prompt,
      dtype,
      generator=None,
  ):
      batch_size = batch_size * num_images_per_prompt
  
      if image.shape[1] == 4:
          init_latents = image
      else:
          init_latents = self.vae_encoder(sample=image)[
              0
          ] * self.vae_decoder.config.get("scaling_factor", 0.18215)
  
      if (
          batch_size > init_latents.shape[0]
          and batch_size % init_latents.shape[0] == 0
      ):
          # expand init_latents for batch_size
          additional_image_per_prompt = (
              batch_size // init_latents.shape[0]
          )
          init_latents = np.concatenate(
              [init_latents] * additional_image_per_prompt, axis=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 = np.concatenate([init_latents], axis=0)
  
      # add noise to latents using the timesteps
      noise = generator.randn(*init_latents.shape).astype(dtype)
      init_latents = self.scheduler.add_noise(
          torch.from_numpy(init_latents),
          torch.from_numpy(noise),
          torch.from_numpy(timestep),
      )
      return init_latents.numpy()
  
file_path: optimum/optimum/pipelines/diffusers/pipeline_stable_diffusion_xl_img2img.py
imports: import inspect
  import logging
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import PIL
  import torch
  from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
  from .pipeline_utils import DiffusionPipelineMixin, rescale_noise_cfg
label_desc: Use image node_id 4 for calling the StableDiffusionXLImg2ImgPipelineMixin obj's underlying member method code with example usage: obj.prepare_latents(image, timestep, batch_size, num_images_per_prompt, dtype, generator) and returns: init_latents