code: def _load_best_model(self):
      model = (
          self.model_wrapped
          if is_sagemaker_mp_enabled()
          else self.model
      )
      logger.info(
          f"Loading best adapter(s) from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
      )
      # attempt to re-load all adapters from checkpoint
      for adapter in model.adapters_config.adapters:
          adapter_dir = os.path.join(
              self.state.best_model_checkpoint, adapter
          )
          if os.path.exists(adapter_dir):
              model.load_adapter(adapter_dir)
      if self.train_adapter_fusion:
          logger.info(
              f"Loading best adapter fusion(s) from {self.state.best_model_checkpoint} (score:"
              f" {self.state.best_metric})."
          )
          # attempt to re-load all adapter fusions from checkpoint
          for fusion in model.adapters_config.fusions:
              fusion_dir = os.path.join(
                  self.state.best_model_checkpoint, fusion
              )
              if os.path.exists(fusion_dir):
                  model.load_adapter_fusion(fusion_dir)
      model.to(self.args.device)
  
imports: import os
  import re
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.utils.data.dataset import Dataset
  from transformers import PreTrainedModel, Seq2SeqTrainer, Trainer, __version__
  from transformers.configuration_utils import PretrainedConfig
  from transformers.data.data_collator import DataCollator
  from transformers.modeling_utils import unwrap_model
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
  from transformers.trainer_utils import EvalPrediction
  from transformers.training_args import TrainingArguments
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, is_sagemaker_mp_enabled, logging
  from .composition import AdapterCompositionBlock, Fuse
label_desc: Use image node_id 8 for calling the AdapterTrainer obj's underlying member method code with example usage: obj._load_best_model() without return types