code: def training_step(self, batch, batch_idx):
      inputs = {
          "input_ids": batch[0],
          "attention_mask": batch[1],
          "labels": batch[3],
      }
  
      if self.config.model_type not in ["distilbert", "bart"]:
          inputs["token_type_ids"] = (
              batch[2]
              if self.config.model_type in ["bert", "xlnet", "albert"]
              else None
          )
  
      outputs = self(**inputs)
      loss = outputs[0]
  
      lr_scheduler = self.trainer.lr_schedulers[0]["scheduler"]
      tensorboard_logs = {
          "loss": loss,
          "rate": lr_scheduler.get_last_lr()[-1],
      }
      return {"loss": loss, "log": tensorboard_logs}
  
imports: import argparse
  import glob
  import logging
  import os
  import time
  from argparse import Namespace
  import numpy
  import torch
  from torch.utils.data import DataLoader, TensorDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
  from transformers import glue_compute_metrics
  from transformers import glue_convert_examples_to_features
  from transformers import glue_output_modes
  from transformers import glue_processors
  from transformers import glue_tasks_num_labels
label_desc: Use image node_id 3 for calling the GLUETransformer obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict