code: def prior_preserving_loss(model_pred, target, weight):
      # Chunk the noise and model_pred into two parts and compute
      # the loss on each part separately.
      model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
      target, target_prior = torch.chunk(target, 2, dim=0)
  
      # Compute instance loss
      loss = F.mse_loss(
          model_pred.float(), target.float(), reduction="mean"
      )
  
      # Compute prior loss
      prior_loss = F.mse_loss(
          model_pred_prior.float(),
          target_prior.float(),
          reduction="mean",
      )
  
      # Add the prior loss to the instance loss.
      return loss + weight * prior_loss
  
imports: from typing import Dict
  import itertools
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
  from diffusers.loaders import LoraLoaderMixin, text_encoder_lora_state_dict
  from diffusers.models.attention_processor import AttnAddedKVProcessor, AttnAddedKVProcessor2_0, LoRAAttnAddedKVProcessor, LoRAAttnProcessor, LoRAAttnProcessor2_0, SlicedAttnAddedKVProcessor
  from diffusers.utils.import_utils import is_xformers_available
  from ray.train import ScalingConfig
  from ray import train
  from ray.train.torch import TorchTrainer
  import torch
  import torch.nn.functional
  from torch.nn.utils import clip_grad_norm_
  from transformers import CLIPTextModel
  from dataset import collate, get_train_dataset
  from flags import train_arguments
label_desc: Use image node_id 1 for calling a global function with example usage: prior_preserving_loss(model_pred, target, weight) and returns: unknown