code: def predict_batch(self, texts=None):
      if texts:
          dl = self.data.get_dl_from_texts(texts)
      else:
          dl = self.data.test_dl
  
      all_summaries = []
  
      self.model.eval()
      for step, batch in enumerate(dl):
          # batch = tuple(t.to(self.device) for t in batch)
  
          batch_data = self.predictor.translate_batch(batch)
          translations = self.predictor.from_batch(batch_data)
  
          summaries = [format_summary(t) for t in translations]
          all_summaries.extend(summaries)
  
      return all_summaries
  
imports: import os
  from .data_abs import BertAbsDataBunch
  from .learner_util import Learner
  from torch import nn
  from typing import List
  import torch
  from box import Box
  from tokenizers import BertWordPieceTokenizer
  from .summarisation import BertAbs, build_predictor
  from .summarisation import BertAbsConfig
  from fastprogress.fastprogress import master_bar, progress_bar
  import numpy
  import pandas
  from pathlib import Path
label_desc: Use image node_id 5 for calling the BertAbsLearner obj's underlying member method code with example usage: obj.predict_batch(texts) and returns: all_summaries