code: def _validate(self, val_iter):
      # Set model to evaluation mode and disable gradient computation
      running_loss = 0
      self.model.eval()
      with torch.no_grad():
          for batch in val_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]
  
              batch_size = batch[0].size(0)
  
              loss = self.model(**inputs)[0]
  
              running_loss += loss.item() * batch_size
  
      return running_loss / len(val_iter.dataset)
  
imports: import os
  import copy
  import logging
  from packaging import version
  from .data_cls import BertDataBunch, InputExample, InputFeatures
  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 .bert_layers import BertLayerNorm
  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
  from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState, CallbackHandler, DefaultFlowCallback
label_desc: Use image node_id 10 for calling the BertLearner obj's underlying member method code with example usage: obj._validate(val_iter) and returns: unknown