code: def predict_batch(self, texts=None, verbose=True):
      if verbose:
          if self.logger is None:
              self.logger = logging.getLogger(__name__)
      if texts:
          if verbose:
              self.logger.info(
                  "---PROGRESS-STATUS---: Tokenizing input texts..."
              )
          dl = self.data.get_dl_from_texts(texts)
          if verbose:
              self.logger.info(
                  "---PROGRESS-STATUS---: Tokenizing input texts...DONE"
              )
      elif self.data.test_dl:
          dl = self.data.test_dl
      else:
          dl = self.data.val_dl
  
      all_logits = None
  
      self.model.eval()
      for step, batch in enumerate(dl):
          if verbose:
              self.logger.info(
                  "---PROGRESS-STATUS---: Predicting batch {}/{}".format(
                      step + 1, len(dl)
                  )
              )
          batch = tuple(t.to(self.device) for t in batch)
  
          inputs = {
              "input_ids": batch[0],
              "attention_mask": batch[1],
              "labels": None,
          }
  
          if self.model_type in ["bert", "xlnet"]:
              inputs["token_type_ids"] = batch[2]
  
          with torch.no_grad():
              outputs = self.model(**inputs)
              logits = outputs[0]
              if self.multi_label:
                  logits = logits.sigmoid()
              # elif len(self.data.labels) == 2:
              #     logits = logits.sigmoid()
              else:
                  logits = logits.softmax(dim=1)
  
          if all_logits is None:
              all_logits = logits.detach().cpu().numpy()
          else:
              all_logits = np.concatenate(
                  (all_logits, logits.detach().cpu().numpy()), axis=0
              )
  
      result_df = pd.DataFrame(all_logits, columns=self.data.labels)
      results = result_df.to_dict(orient="records")
  
      out_results = [
          sorted(x.items(), key=lambda kv: kv[1], reverse=True)
          for x in results
      ]
  
      # self.control = self.callback_handler.on_predict(
      #     self.training_arguments, self.state, self.control, results=out_results
      # )
  
      if verbose:
          self.logger.info(
              "---PROGRESS-STATUS---: Predicting batch...DONE"
          )
      return out_results
  
imports: import os
  import copy
  import logging
  from packaging import version
  from .data_cls import BertDataBunch
  from tqdm.autonotebook import tqdm
  import matplotlib.pyplot
  from .learner_util import Learner
  from torch import nn
  from typing import List, Optional
  from .modeling import BertForMultiLabelSequenceClassification, XLNetForMultiLabelSequenceClassification, RobertaForMultiLabelSequenceClassification, DistilBertForMultiLabelSequenceClassification, CamembertForMultiLabelSequenceClassification, AlbertForMultiLabelSequenceClassification, ElectraForMultiLabelSequenceClassification, FlaubertForMultiLabelSequenceClassification
  from fastprogress.fastprogress import master_bar, progress_bar
  import torch
  import pandas
  import numpy
  from sklearn.metrics import roc_curve, auc
  from pathlib import Path
  from torch.optim.lr_scheduler import _LRScheduler, Optimizer
  from tensorboardX import SummaryWriter
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer, AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer, DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer, ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer, FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer
  from transformers import AutoModelForSequenceClassification, AutoConfig, TrainingArguments
  from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState, CallbackHandler, DefaultFlowCallback
label_desc: Use image node_id 6 for calling the BertLearner obj's underlying member method code with example usage: obj.predict_batch(texts, verbose) and returns: out_results