code: def predict(self, text, group=True, exclude_entities=["O"]):
      if exclude_entities is None:
          exclude_entities = []
      label_list = self.data.labels
  
      tokenizer = self.data.tokenizer
      tokens = tokenizer.tokenize(
          tokenizer.decode(tokenizer.encode(text))
      )
      inputs = tokenizer.encode(text, return_tensors="pt")
      inputs = inputs.to(self.device)
  
      model = self.model.to(self.device)
  
      outputs = model(inputs)[0]
      outputs = outputs.softmax(dim=2)
      predictions = torch.argmax(outputs, dim=2)
  
      preds = [
          (token, label_list[prediction], output[prediction])
          for token, output, prediction in zip(
              tokens, outputs[0].tolist(), predictions[0].tolist()
          )
      ][1:-1]
  
      preds = [
          {
              "index": index,
              "word": prediction[0],
              "entity": prediction[1],
              "score": prediction[2],
          }
          for index, prediction in enumerate(preds)
      ]
  
      if group is True:
          preds = group_entities(preds)
  
      out_preds = []
      for pred in preds:
          if pred["entity"] not in exclude_entities:
              try:
                  pred["entity"] = pred["entity"].split("-")[1]
              except Exception:
                  pass
  
              out_preds.append(pred)
  
      return out_preds
  
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 6 for calling the BertNERLearner obj's underlying member method code with example usage: obj.predict(text, group, exclude_entities) and returns: out_preds