code: def setup(self, mode):
      if mode == "test":
          self.dataset_size = len(self.test_dataloader().dataset)
      else:
          self.train_loader = self.get_dataloader(
              "train", self.hparams.train_batch_size, shuffle=True
          )
          self.dataset_size = len(self.train_dataloader().dataset)
  
imports: import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  import pytorch_lightning
  from pytorch_lightning.utilities import rank_zero_info
  from transformers import AdamW, AutoConfig, AutoModel, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoModelWithLMHead, AutoTokenizer, PretrainedConfig, PreTrainedTokenizer
  from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup
  from transformers.utils.versions import require_version
label_desc: Use image node_id 8 for calling the BaseTransformer obj's underlying member method code with example usage: obj.setup(mode) without return types