code: def _load_and_sync_parameters(self):
      resume_checkpoint = (
          find_resume_checkpoint() or self.resume_checkpoint
      )
  
      if resume_checkpoint:
          self.resume_step = parse_resume_step_from_filename(
              resume_checkpoint
          )
          if dist.get_rank() == 0:
              logger.log(
                  f"loading model from checkpoint: {resume_checkpoint}..."
              )
              self.model.load_state_dict(
                  dist_util.load_state_dict(
                      resume_checkpoint, map_location=dist_util.dev()
                  )
              )
  
      dist_util.sync_params(self.model.parameters())
  
imports: import copy
  import functools
  import os
  import blobfile
  import numpy
  import torch
  import torch.distributed
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.optim import AdamW
  from .None import dist_util, logger
  from .fp16_util import make_master_params, master_params_to_model_params, model_grads_to_master_grads, unflatten_master_params, zero_grad
  from .nn import update_ema
  from .resample import LossAwareSampler, UniformSampler
label_desc: Use image node_id 2 for calling the TrainLoop obj's underlying member method code with example usage: obj._load_and_sync_parameters() without return types