code: def prepare_output_and_logger(args):
      if not args.model_path:
          if os.getenv("OAR_JOB_ID"):
              unique_str = os.getenv("OAR_JOB_ID")
          else:
              unique_str = str(uuid.uuid4())
          args.model_path = os.path.join("./output/", unique_str[0:10])
  
      # Set up output folder
      print("Output folder: {}".format(args.model_path))
      os.makedirs(args.model_path, exist_ok=True)
      with open(
          os.path.join(args.model_path, "cfg_args"), "w"
      ) as cfg_log_f:
          cfg_log_f.write(str(Namespace(**vars(args))))
  
      # Create Tensorboard writer
      tb_writer = None
      if TENSORBOARD_FOUND:
          tb_writer = SummaryWriter(args.model_path)
      else:
          print("Tensorboard not available: not logging progress")
      return tb_writer
  
file_path: gaussian-splatting/train.py
imports: import os
  import torch
  from random import randint
  from utils.loss_utils import l1_loss, ssim
  from gaussian_renderer import render, network_gui
  import sys
  from scene import Scene, GaussianModel
  from utils.general_utils import safe_state
  import uuid
  from tqdm import tqdm
  from utils.image_utils import psnr
  from argparse import ArgumentParser, Namespace
  from arguments import ModelParams, PipelineParams, OptimizationParams
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_output_and_logger(args) and returns: tb_writer