code: def enable_gradient_checkpointing(unet, text_encoders, config):
      if not config.disable_gradient_checkpointing:
          logger.info("Enabling gradient checkpointing.")
          unet.enable_gradient_checkpointing()
          if config.train_text_encoder:
              for i in range(len(text_encoders)):
                  text_encoders[i].gradient_checkpointing_enable()
  
imports: import hashlib
  import itertools
  import os
  from pathlib import Path
  from typing import Dict
  import torch
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionXLPipeline, UNet2DConditionModel
  from diffusers.utils.import_utils import is_xformers_available
  from packaging import version
  from tqdm import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  from autotrain import logger
  from autotrain.trainers.dreambooth.datasets import PromptDataset
label_desc: Use image node_id 9 for calling a global function with example usage: enable_gradient_checkpointing(unet, text_encoders, config) without return types