code: def _load_adapter_fusions(self, resume_from_checkpoint):
      for file_name in os.listdir(resume_from_checkpoint):
          if os.path.isdir(
              os.path.join(resume_from_checkpoint, file_name)
          ):
              if "," in file_name:
                  self.model.load_adapter_fusion(
                      os.path.join(resume_from_checkpoint, file_name)
                  )
  
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 6 for calling the AdapterTrainer obj's underlying member method code with example usage: obj._load_adapter_fusions(resume_from_checkpoint) without return types