code: def _setup_xl(self):
      # Handle instance prompt.
      instance_time_ids = self.compute_time_ids()
      if not self.config.train_text_encoder:
          (
              instance_prompt_hidden_states,
              instance_pooled_prompt_embeds,
          ) = self.compute_text_embeddings(self.config.prompt)
  
      # Handle class prompt for prior-preservation.
      if self.config.prior_preservation:
          class_time_ids = self.compute_time_ids()
          if not self.config.train_text_encoder:
              (
                  class_prompt_hidden_states,
                  class_pooled_prompt_embeds,
              ) = self.compute_text_embeddings(self.config.class_prompt)
  
      self.add_time_ids = instance_time_ids
      if self.config.prior_preservation:
          self.add_time_ids = torch.cat(
              [self.add_time_ids, class_time_ids], dim=0
          )
  
      if not self.config.train_text_encoder:
          self.prompt_embeds = instance_prompt_hidden_states
          self.unet_add_text_embeds = instance_pooled_prompt_embeds
          if self.config.prior_preservation:
              self.prompt_embeds = torch.cat(
                  [self.prompt_embeds, class_prompt_hidden_states],
                  dim=0,
              )
              self.unet_add_text_embeds = torch.cat(
                  [
                      self.unet_add_text_embeds,
                      class_pooled_prompt_embeds,
                  ],
                  dim=0,
              )
      else:
          self.tokens_one = utils.tokenize_prompt(
              self.tokenizers[0], self.config.prompt
          ).input_ids
          self.tokens_two = utils.tokenize_prompt(
              self.tokenizers[1], self.config.prompt
          ).input_ids
          if self.config.prior_preservation:
              class_tokens_one = utils.tokenize_prompt(
                  self.tokenizers[0], self.config.class_prompt
              ).input_ids
              class_tokens_two = utils.tokenize_prompt(
                  self.tokenizers[1], self.config.class_prompt
              ).input_ids
              self.tokens_one = torch.cat(
                  [self.tokens_one, class_tokens_one], dim=0
              )
              self.tokens_two = torch.cat(
                  [self.tokens_two, class_tokens_two], dim=0
              )
  
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 4 for calling the Trainer obj's underlying member method code with example usage: obj._setup_xl() without return types