code: def on_train_begin(
      self,
      args: TrainingArguments,
      state: TrainerState,
      control: TrainerControl,
      **kwargs,
  ):
      model = kwargs.pop("model")
      model_frozen = getattr(model.base_model, "model_frozen", False)
      if not model_frozen:
          raise ValueError(
              "The pre-trained model weights are not frozen. For training adapters, please call the train_adapter()"
              " method"
          )
  
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 2 for calling the AdapterTrainerCallback obj's underlying member method code with example usage: obj.on_train_begin(args, state, control) without return types