code: def __init__(self, trainer):
      super().__init__()
      self.trainer = trainer
  
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 1 to create a new AdapterTrainerCallback object from inherited base classes: TrainerCallback with example: obj = AdapterTrainerCallback(trainer)