code: def _resume_from_checkpoint(self):
      if self.config.resume_from_checkpoint != "latest":
          path = os.path.basename(self.config.resume_from_checkpoint)
      else:
          # Get the mos recent checkpoint
          dirs = os.listdir(self.config.project_name)
          dirs = [d for d in dirs if d.startswith("checkpoint")]
          dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
          path = dirs[-1] if len(dirs) > 0 else None
  
      if path is None:
          self.accelerator.print(
              f"Checkpoint '{self.config.resume_from_checkpoint}' does not exist. Starting a new training run."
          )
          self.config.resume_from_checkpoint = None
      else:
          self.accelerator.print(f"Resuming from checkpoint {path}")
          self.accelerator.load_state(
              os.path.join(self.config.project_name, path)
          )
          self.global_step = int(path.split("-")[1])
  
          resume_global_step = (
              self.global_step * self.config.gradient_accumulation
          )
          self.first_epoch = (
              self.global_step // self.num_update_steps_per_epoch
          )
          self.resume_step = resume_global_step % (
              self.num_update_steps_per_epoch
              * self.config.gradient_accumulation
          )
  
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 5 for calling the Trainer obj's underlying member method code with example usage: obj._resume_from_checkpoint() without return types