code: def validate(
      self,
      prefix="SQUAD",
      n_best_size=20,
      max_answer_length=30,
      verbose_logging=False,
      null_score_diff_threshold=0.0,
  ):
      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, eval_accuracy = 0, 0
      nb_eval_steps, nb_eval_examples = 0, 0
  
      preds = None
      out_label_ids = None
      all_results = []
      for step, batch in enumerate(progress_bar(self.data.val_dl)):
          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],
                  "token_type_ids": None
                  if self.model_type == "xlm"
                  else batch[2],  # XLM don't use segment_ids
              }
              example_indices = batch[3]
              if self.model_type in ["xlnet", "xlm"]:
                  inputs.update(
                      {"cls_index": batch[4], "p_mask": batch[5]}
                  )
  
              outputs = self.model(**inputs)
              tmp_eval_loss, logits = outputs[:2]
              eval_loss += tmp_eval_loss.mean().item()
  
          for i, example_index in enumerate(example_indices):
              eval_feature = self.data.val_features[
                  example_index.item()
              ]
              unique_id = int(eval_feature.unique_id)
              if self.model_type in ["xlnet", "xlm"]:
                  # XLNet uses a more complex post-processing procedure
                  result = RawResultExtended(
                      unique_id=unique_id,
                      start_top_log_probs=to_list(outputs[0][i]),
                      start_top_index=to_list(outputs[1][i]),
                      end_top_log_probs=to_list(outputs[2][i]),
                      end_top_index=to_list(outputs[3][i]),
                      cls_logits=to_list(outputs[4][i]),
                  )
              else:
                  result = RawResult(
                      unique_id=unique_id,
                      start_logits=to_list(outputs[0][i]),
                      end_logits=to_list(outputs[1][i]),
                  )
              all_results.append(result)
  
          nb_eval_steps += 1
  
      # Compute predictions
      output_prediction_file = os.path.join(
          self.validation_out, "predictions_{}.json".format(prefix)
      )
      output_nbest_file = os.path.join(
          self.validation_out,
          "nbest_predictions_{}.json".format(prefix),
      )
      if self.data.version_2_with_negative:
          output_null_log_odds_file = os.path.join(
              self.validation_out, "null_odds_{}.json".format(prefix)
          )
      else:
          output_null_log_odds_file = None
  
      if self.model_type in ["xlnet", "xlm"]:
          # XLNet uses a more complex post-processing procedure
          write_predictions_extended(
              self.data.val_examples,
              self.data.val_features,
              all_results,
              n_best_size,
              max_answer_length,
              output_prediction_file,
              output_nbest_file,
              output_null_log_odds_file,
              self.data.val_file,
              self.model.config.start_n_top,
              self.model.config.end_n_top,
              self.data.version_2_with_negative,
              self.data.tokenizer,
              verbose_logging,
              self.logger,
          )
      else:
          write_predictions(
              self.data.val_examples,
              self.data.val_features,
              all_results,
              n_best_size,
              max_answer_length,
              self.data.do_lower_case,
              output_prediction_file,
              output_nbest_file,
              output_null_log_odds_file,
              verbose_logging,
              self.data.version_2_with_negative,
              null_score_diff_threshold,
              self.logger,
          )
  
      # Evaluate with the official SQuAD script
      evaluate_options = EVAL_OPTS(
          data_file=self.data.validation_file,
          pred_file=output_prediction_file,
          na_prob_file=output_null_log_odds_file,
      )
      results = evaluate_on_squad(evaluate_options)
      return results
  
imports: import os
  import torch
  import pandas
  import numpy
  from pathlib import Path
  import collections
  import math
  from io import open
  import json
  from .data_qa import BertQADataBunch
  from .learner_util import Learner
  from fastprogress.fastprogress import master_bar, progress_bar
  from tensorboardX import SummaryWriter
  from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
  from transformers import BertConfig, BertForQuestionAnswering, XLNetConfig, XLNetForQuestionAnswering, XLMConfig, XLMForQuestionAnswering, DistilBertConfig, DistilBertForQuestionAnswering
  from .utils_squad_evaluate import EVAL_OPTS, main
label_desc: Use image node_id 4 for calling the BertQALearner obj's underlying member method code with example usage: obj.validate(prefix, n_best_size, max_answer_length, verbose_logging, null_score_diff_threshold) and returns: results