code: def prepare_latents(
      self, image, timestep, batch_size, dtype, device, generator=None
  ):
      if not isinstance(image, torch.Tensor):
          raise ValueError(
              f"`image` has to be of type `torch.Tensor` but is {type(image)}"
          )
  
      image = image.to(device=device, dtype=dtype)
  
      if isinstance(generator, list):
          init_latents = [
              self.vae.encode(image[i : i + 1]).latent_dist.sample(
                  generator[i]
              )
              for i in range(batch_size)
          ]
          init_latents = torch.cat(init_latents, dim=0)
      else:
          init_latents = self.vae.encode(image).latent_dist.sample(
              generator
          )
  
      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      init_latents = 0.18215 * init_latents
      init_latents = init_latents.repeat_interleave(batch_size, dim=0)
  
      noise = randn_tensor(
          init_latents.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 inspect
  from typing import Optional, Union
  import numpy
  import PIL.Image
  import torch
  from torch.nn import functional
  from torchvision import transforms
  from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.utils import PIL_INTERPOLATION
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 9 for calling the CLIPGuidedImagesMixingStableDiffusion obj's underlying member method code with example usage: obj.prepare_latents(image, timestep, batch_size, dtype, device, generator) and returns: latents