code: def _clip_gradients(self):
      if self.accelerator.sync_gradients:
          if len(self.text_lora_parameters) == 0:
              params_to_clip = self.unet_lora_parameters
          elif len(self.text_lora_parameters) == 1:
              params_to_clip = itertools.chain(
                  self.unet_lora_parameters,
                  self.text_lora_parameters[0],
              )
          elif len(self.text_lora_parameters) == 2:
              params_to_clip = itertools.chain(
                  self.unet_lora_parameters,
                  self.text_lora_parameters[0],
                  self.text_lora_parameters[1],
              )
          else:
              raise ValueError(
                  "More than 2 text encoders are not supported."
              )
          self.accelerator.clip_grad_norm_(
              params_to_clip, self.config.max_grad_norm
          )
  
imports: import itertools
  import math
  import os
  import shutil
  import torch
  import torch.nn.functional
  from diffusers import StableDiffusionXLPipeline
  from diffusers.loaders import LoraLoaderMixin, text_encoder_lora_state_dict
  from diffusers.optimization import get_scheduler
  from huggingface_hub import create_repo, upload_folder
  from tqdm import tqdm
  from autotrain import logger
  from autotrain.trainers.dreambooth import utils
label_desc: Use image node_id 7 for calling the Trainer obj's underlying member method code with example usage: obj._clip_gradients() without return types