code: def _train_batch(self, train_iter):
      self.model.train()
      total_loss = None  # for late initialization
  
      self.optimizer.zero_grad()
      for i in range(self.grad_accumulation_steps):
          batch = next(train_iter)
  
          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()
              )  # mean() to average on multi-gpu parallel training
  
          loss /= self.grad_accumulation_steps
  
          if self.is_fp16:
              self.scaler.scale(loss).backward()
          else:
              loss.backward()
  
          if total_loss is None:
              total_loss = loss
          else:
              total_loss += loss
  
      self.optimizer.step()
  
      return total_loss.item()
  
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 9 for calling the BertLearner obj's underlying member method code with example usage: obj._train_batch(train_iter) and returns: total_loss