code: def predict_batch(
      self,
      group=True,
      exclude_entities=["O"],
      return_metrics=True,
      verbose=True,
  ):
      if verbose:
          if self.logger is None:
              self.logger = logging.getLogger(__name__)
  
      if self.data.test_dl:
          dl = self.data.test_dl
      else:
          dl = self.data.val_dl
  
      label_list = self.data.labels
      tokenizer = self.data.tokenizer
      results = self.get_trainer().predict(test_dataset=dl.dataset)
      outputs = torch.Tensor(results.predictions)
      outputs = outputs.softmax(dim=2)
      predictions = torch.argmax(outputs, dim=2)
  
      preds = []
      for index, prediction in enumerate(predictions):
          text = tokenizer.decode(
              dl.dataset[index].input_ids, skip_special_tokens=True
          )
          tokens = tokenizer.tokenize(
              tokenizer.decode(tokenizer.encode(text))
          )
  
          processed_pred = [
              (token, label_list[pred], out[pred])
              for token, out, pred in zip(
                  tokens, outputs[index].tolist(), prediction.tolist()
              )
          ][1:-1]
  
          processed_pred = [
              {
                  "index": index,
                  "word": pred[0],
                  "entity": pred[1],
                  "score": pred[2],
              }
              for index, pred in enumerate(processed_pred)
          ]
          if group is True:
              processed_pred = group_entities(processed_pred)
  
          out_preds = []
          for pred in processed_pred:
              if pred["entity"] not in exclude_entities:
                  try:
                      pred["entity"] = pred["entity"].split("-")[1]
                  except Exception:
                      pass
  
                  out_preds.append(pred)
  
          preds.append({"text": text, "labels": out_preds})
      out_res = {
          "predictions": preds,
      }
      if return_metrics:
          out_res["metrics"] = results.metrics
      return out_res
  
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 5 for calling the BertNERLearner obj's underlying member method code with example usage: obj.predict_batch(group, exclude_entities, return_metrics, verbose) and returns: out_res