code: def __init__(
      self,
      unet,
      vae,
      train_dataloader,
      train_dataset,
      text_encoders,
      config,
      optimizer,
      accelerator,
      noise_scheduler,
      weight_dtype,
      text_lora_parameters,
      unet_lora_parameters,
      tokenizers,
  ):
      self.train_dataloader = train_dataloader
      self.config = config
      self.optimizer = optimizer
      self.accelerator = accelerator
      self.unet = unet
      self.vae = vae
      self.noise_scheduler = noise_scheduler
      self.train_dataset = train_dataset
      self.weight_dtype = weight_dtype
      self.text_lora_parameters = text_lora_parameters
      self.unet_lora_parameters = unet_lora_parameters
      self.tokenizers = tokenizers
      self.text_encoders = text_encoders
  
      if self.config.xl:
          self._setup_xl()
  
      self.text_encoder1 = text_encoders[0]
      self.text_encoder2 = None
      if len(text_encoders) == 2:
          self.text_encoder2 = text_encoders[1]
  
      overrode_max_train_steps = False
      self.num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / config.gradient_accumulation
      )
      if self.config.num_steps is None:
          self.config.num_steps = (
              self.config.epochs * self.num_update_steps_per_epoch
          )
          overrode_max_train_steps = True
  
      self.scheduler = get_scheduler(
          self.config.scheduler,
          optimizer=self.optimizer,
          num_warmup_steps=self.config.warmup_steps
          * self.accelerator.num_processes,
          num_training_steps=self.config.num_steps
          * self.accelerator.num_processes,
          num_cycles=self.config.num_cycles,
          power=self.config.lr_power,
      )
  
      if self.config.train_text_encoder:
          if len(text_encoders) == 1:
              (
                  self.unet,
                  self.text_encoder1,
                  self.optimizer,
                  self.train_dataloader,
                  self.scheduler,
              ) = self.accelerator.prepare(
                  self.unet,
                  self.text_encoder1,
                  self.optimizer,
                  self.train_dataloader,
                  self.scheduler,
              )
          elif len(text_encoders) == 2:
              (
                  self.unet,
                  self.text_encoder1,
                  self.text_encoder2,
                  self.optimizer,
                  self.train_dataloader,
                  self.scheduler,
              ) = self.accelerator.prepare(
                  self.unet,
                  self.text_encoder1,
                  self.text_encoder2,
                  self.optimizer,
                  self.train_dataloader,
                  self.scheduler,
              )
  
      else:
          (
              self.unet,
              self.optimizer,
              self.train_dataloader,
              self.scheduler,
          ) = accelerator.prepare(
              self.unet,
              self.optimizer,
              self.train_dataloader,
              self.scheduler,
          )
  
      self.num_update_steps_per_epoch = math.ceil(
          len(self.train_dataloader) / self.config.gradient_accumulation
      )
      if overrode_max_train_steps:
          self.config.num_steps = (
              self.config.epochs * self.num_update_steps_per_epoch
          )
      # Afterwards we recalculate our number of training epochs
      self.config.epochs = math.ceil(
          self.config.num_steps / self.num_update_steps_per_epoch
      )
  
      if self.accelerator.is_main_process:
          self.accelerator.init_trackers("dreambooth")
  
      self.total_batch_size = (
          self.config.batch_size
          * self.accelerator.num_processes
          * self.config.gradient_accumulation
      )
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(self.train_dataset)}")
      logger.info(
          f"  Num batches each epoch = {len(self.train_dataloader)}"
      )
      logger.info(f"  Num Epochs = {self.config.epochs}")
      logger.info(
          f"  Instantaneous batch size per device = {config.batch_size}"
      )
      logger.info(
          f"  Total train batch size (w. parallel, distributed & accumulation) = {self.total_batch_size}"
      )
      logger.info(
          f"  Gradient Accumulation steps = {self.config.gradient_accumulation}"
      )
      logger.info(
          f"  Total optimization steps = {self.config.num_steps}"
      )
      logger.info(f"  Training config = {self.config}")
      self.global_step = 0
      self.first_epoch = 0
  
      if config.resume_from_checkpoint:
          self._resume_from_checkpoint()
  
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 1 to create a new Trainer object  with example: obj = Trainer(unet, vae, train_dataloader, train_dataset, text_encoders, config, optimizer, accelerator, noise_scheduler, weight_dtype, text_lora_parameters, unet_lora_parameters, tokenizers)