code: def _feature_file(self, mode):
      return os.path.join(
          self.hparams.data_dir,
          "cached_{}_{}_{}".format(
              mode,
              list(
                  filter(
                      None, self.hparams.model_name_or_path.split("/")
                  )
              ).pop(),
              str(self.hparams.max_seq_length),
          ),
      )
  
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 13 for calling the BaseTransformer obj's underlying member method code with example usage: obj._feature_file(mode) and returns: os