code: def validate(self, quiet=False, loss_only=False, return_preds=False):
      if quiet is False:
          self.logger.info("Running evaluation")
          self.logger.info(
              "  Num examples = %d", len(self.data.val_dl.dataset)
          )
          self.logger.info(
              "  Batch size = %d", self.data.val_batch_size
          )
  
      all_logits = None
      all_labels = None
  
      eval_loss = 0
      nb_eval_steps, nb_eval_examples = 0, 0
  
      preds = None
      out_label_ids = None
  
      validation_scores = {
          metric["name"]: 0.0 for metric in self.metrics
      }
  
      iterator = (
          self.data.val_dl if quiet else progress_bar(self.data.val_dl)
      )
  
      for step, batch in enumerate(iterator):
          self.model.eval()
          batch = tuple(t.to(self.device) for t in batch)
  
          with torch.no_grad():
              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]
  
              outputs = self.model(**inputs)
              tmp_eval_loss, logits = outputs[:2]
  
              eval_loss += tmp_eval_loss.mean().item()
  
          nb_eval_steps += 1
          nb_eval_examples += inputs["input_ids"].size(0)
  
          if all_logits is None:
              all_logits = logits
          else:
              all_logits = torch.cat((all_logits, logits), 0)
  
          if all_labels is None:
              all_labels = inputs["labels"]
          else:
              all_labels = torch.cat((all_labels, inputs["labels"]), 0)
  
          if preds is None:
              preds = logits.detach().cpu().numpy()
              out_label_ids = inputs["labels"].detach().cpu().numpy()
          else:
              preds = np.append(
                  preds, logits.detach().cpu().numpy(), axis=0
              )
              out_label_ids = np.append(
                  out_label_ids,
                  inputs["labels"].detach().cpu().numpy(),
                  axis=0,
              )
  
      eval_loss = eval_loss / nb_eval_steps
  
      results = {"loss": eval_loss}
  
      if return_preds:
          results["y_preds"] = np.argmax(
              all_logits.detach().cpu().numpy(), axis=1
          )
          results["y_true"] = all_labels.detach().cpu().numpy()
  
      if loss_only is False:
          # Evaluation metrics
          for metric in self.metrics:
              validation_scores[metric["name"]] = metric["function"](
                  all_logits, all_labels, labels=self.data.labels
              )
          results.update(validation_scores)
  
      return results
  
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 5 for calling the BertLearner obj's underlying member method code with example usage: obj.validate(quiet, loss_only, return_preds) and returns: results