code: def default_trainer_args():
      argspec = dict(inspect.signature(Trainer.__init__).parameters)
      argspec.pop("self")
      default_args = {
          param: argspec[param].default
          for param in argspec
          if argspec[param] != Parameter.empty
      }
      return default_args
  
imports: import argparse
  import datetime
  import glob
  import inspect
  import os
  import sys
  from inspect import Parameter
  from typing import Union
  import numpy
  import pytorch_lightning
  import torch
  import torchvision
  import wandb
  from matplotlib import pyplot
  from natsort import natsorted
  from omegaconf import OmegaConf
  from packaging import version
  from PIL import Image
  from pytorch_lightning import seed_everything
  from pytorch_lightning.callbacks import Callback
  from pytorch_lightning.loggers import WandbLogger
  from pytorch_lightning.trainer import Trainer
  from pytorch_lightning.utilities import rank_zero_only
  from sgm.util import exists, instantiate_from_config, isheatmap
label_desc: Use image node_id 1 for calling a global function with example usage: default_trainer_args() and returns: default_args