code: def training_step(self, batch):
      self.model.train()
      batch = tuple(t.to(self.device) for t in batch)
      inputs = {
          "input_ids": batch[0],
          "attention_mask": batch[1],
          "labels": batch[3],
      }
  
      if self.model_type in ["bert", "xlnet"]:
          inputs["token_type_ids"] = batch[2]
  
      if self.is_fp16:
          with autocast():
              outputs = self.model(**inputs)
      else:
          outputs = self.model(**inputs)
  
      loss = outputs[0]
  
      if self.n_gpu > 1:
          loss = loss.mean()
  
      if self.grad_accumulation_steps > 1:
          loss = loss / self.grad_accumulation_steps
  
      if self.is_fp16:
          self.scaler.scale(loss).backward()
      else:
          loss.backward()
  
      return loss
  
imports: import os
  import copy
  import logging
  from packaging import version
  from .data_cls import BertDataBunch
  from tqdm.autonotebook import tqdm
  import matplotlib.pyplot
  from .learner_util import Learner
  from torch import nn
  from typing import List, Optional
  from .modeling import BertForMultiLabelSequenceClassification, XLNetForMultiLabelSequenceClassification, RobertaForMultiLabelSequenceClassification, DistilBertForMultiLabelSequenceClassification, CamembertForMultiLabelSequenceClassification, AlbertForMultiLabelSequenceClassification, ElectraForMultiLabelSequenceClassification, FlaubertForMultiLabelSequenceClassification
  from fastprogress.fastprogress import master_bar, progress_bar
  import torch
  import pandas
  import numpy
  from sklearn.metrics import roc_curve, auc
  from pathlib import Path
  from torch.optim.lr_scheduler import _LRScheduler, Optimizer
  from tensorboardX import SummaryWriter
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer, AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer, DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer, ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer, FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer
  from transformers import AutoModelForSequenceClassification, AutoConfig, TrainingArguments
  from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState, CallbackHandler, DefaultFlowCallback
label_desc: Use image node_id 4 for calling the BertLearner obj's underlying member method code with example usage: obj.training_step(batch) and returns: loss