code: def get_target(scheduler, noise, latents, timesteps):
      """Get the target for loss depending on the prediction type."""
      pred_type = scheduler.config.prediction_type
      if pred_type == "epsilon":
          return noise
      if pred_type == "v_prediction":
          return scheduler.get_velocity(latents, noise, timesteps)
      raise ValueError(f"Unknown prediction type {pred_type}")
  
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 2 for calling a global function with example usage: get_target(scheduler, noise, latents, timesteps) and returns: noise, scheduler