code: def validate(self, quiet=False, loss_only=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
          )
  
      result = self.get_trainer().evaluate()
      return result
  
imports: import os
  import logging
  import torch
  import numpy
  from .data_ner import BertNERDataBunch
  from torch import nn
  from seqeval.metrics import f1_score, precision_score, recall_score
  from typing import Dict, List, Optional, Tuple
  from .learner_util import Learner
  from transformers import AutoConfig, AutoModelForTokenClassification, Trainer, TrainingArguments, EvalPrediction, HfArgumentParser
label_desc: Use image node_id 4 for calling the BertNERLearner obj's underlying member method code with example usage: obj.validate(quiet, loss_only) and returns: result