code: def cond_fn(
      self,
      latents,
      timestep,
      index,
      text_embeddings,
      noise_pred_original,
      original_image_embeddings_clip,
      clip_guidance_scale,
  ):
      latents = latents.detach().requires_grad_()
  
      latent_model_input = self.scheduler.scale_model_input(
          latents, timestep
      )
  
      # predict the noise residual
      noise_pred = self.unet(
          latent_model_input,
          timestep,
          encoder_hidden_states=text_embeddings,
      ).sample
  
      if isinstance(
          self.scheduler,
          (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler),
      ):
          alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
          beta_prod_t = 1 - alpha_prod_t
          # compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          pred_original_sample = (
              latents - beta_prod_t ** (0.5) * noise_pred
          ) / alpha_prod_t ** (0.5)
  
          fac = torch.sqrt(beta_prod_t)
          sample = pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler, LMSDiscreteScheduler):
          sigma = self.scheduler.sigmas[index]
          sample = latents - sigma * noise_pred
      else:
          raise ValueError(
              f"scheduler type {type(self.scheduler)} not supported"
          )
  
      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      sample = 1 / 0.18215 * sample
      image = self.vae.decode(sample).sample
      image = (image / 2 + 0.5).clamp(0, 1)
  
      image = transforms.Resize(self.feature_extractor_size)(image)
      image = self.normalize(image).to(latents.dtype)
  
      image_embeddings_clip = self.clip_model.get_image_features(image)
      image_embeddings_clip = (
          image_embeddings_clip
          / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
      )
  
      loss = (
          spherical_dist_loss(
              image_embeddings_clip, original_image_embeddings_clip
          ).mean()
          * clip_guidance_scale
      )
  
      grads = -torch.autograd.grad(loss, latents)[0]
  
      if isinstance(self.scheduler, LMSDiscreteScheduler):
          latents = latents.detach() + grads * (sigma**2)
          noise_pred = noise_pred_original
      else:
          noise_pred = (
              noise_pred_original - torch.sqrt(beta_prod_t) * grads
          )
      return noise_pred, 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 12 for calling the CLIPGuidedImagesMixingStableDiffusion obj's underlying member method code with example usage: obj.cond_fn(latents, timestep, index, text_embeddings, noise_pred_original, original_image_embeddings_clip, clip_guidance_scale) and returns: noise_pred,  latents