code: def log_validation(
      text_encoder,
      tokenizer,
      prior,
      args,
      accelerator,
      weight_dtype,
      epoch,
  ):
      logger.info("Running validation... ")
  
      pipeline = AutoPipelineForText2Image.from_pretrained(
          args.pretrained_decoder_model_name_or_path,
          prior_prior=accelerator.unwrap_model(prior),
          prior_text_encoder=accelerator.unwrap_model(text_encoder),
          prior_tokenizer=tokenizer,
          torch_dtype=weight_dtype,
      )
      pipeline = pipeline.to(accelerator.device)
      pipeline.set_progress_bar_config(disable=True)
  
      if args.seed is None:
          generator = None
      else:
          generator = torch.Generator(
              device=accelerator.device
          ).manual_seed(args.seed)
  
      images = []
      for i in range(len(args.validation_prompts)):
          with torch.autocast("cuda"):
              image = pipeline(
                  args.validation_prompts[i],
                  prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
                  generator=generator,
                  height=args.resolution,
                  width=args.resolution,
              ).images[0]
  
          images.append(image)
  
      for tracker in accelerator.trackers:
          if tracker.name == "tensorboard":
              np_images = np.stack([np.asarray(img) for img in images])
              tracker.writer.add_images(
                  "validation", np_images, epoch, dataformats="NHWC"
              )
          elif tracker.name == "wandb":
              tracker.log(
                  {
                      "validation": [
                          wandb.Image(
                              image,
                              caption=f"{i}: {args.validation_prompts[i]}",
                          )
                          for i, image in enumerate(images)
                      ]
                  }
              )
          else:
              logger.warn(
                  f"image logging not implemented for {tracker.name}"
              )
  
      del pipeline
      torch.cuda.empty_cache()
  
      return images
  
imports: import argparse
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  import accelerate
  import datasets
  import numpy
  import torch
  import torch.nn.functional
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.state import AcceleratorState, is_initialized
  from accelerate.utils import ProjectConfiguration, set_seed
  from datasets import load_dataset
  from huggingface_hub import create_repo, hf_hub_download, upload_folder
  from modeling_efficient_net_encoder import EfficientNetEncoder
  from packaging import version
  from torchvision import transforms
  from tqdm import tqdm
  from transformers import CLIPTextModel, PreTrainedTokenizerFast
  from transformers.utils import ContextManagers
  from diffusers import AutoPipelineForText2Image, DDPMWuerstchenScheduler
  from diffusers.optimization import get_scheduler
  from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPrior
  from diffusers.training_utils import EMAModel
  from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
  from diffusers.utils.logging import set_verbosity_error, set_verbosity_info
label_desc: Use image node_id 2 for calling a global function with example usage: log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, epoch) and returns: images