code: def get_dl_from_texts(self, texts):
      test_examples = []
      input_data = []
  
      for index, text in enumerate(texts):
          test_examples.append(InputExample(index, text, label=None))
          input_data.append({"id": index, "text": text})
      test_features = convert_examples_to_features(
          test_examples,
          label_list=self.labels,
          tokenizer=self.tokenizer,
          max_seq_length=self.maxlen,
      )
  
      all_input_ids = torch.tensor(
          [f.input_ids for f in test_features], dtype=torch.long
      )
      all_input_mask = torch.tensor(
          [f.input_mask for f in test_features], dtype=torch.long
      )
      all_segment_ids = torch.tensor(
          [f.segment_ids for f in test_features], dtype=torch.long
      )
  
      test_data = TensorDataset(
          all_input_ids, all_input_mask, all_segment_ids
      )
  
      test_sampler = SequentialSampler(test_data)
      return DataLoader(
          test_data, sampler=test_sampler, batch_size=self.bs
      )
  
imports: import pandas
  import os
  import torch
  from pathlib import Path
  import pickle
  from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer
label_desc: Use image node_id 1 for calling the BertDataBunch obj's underlying member method code with example usage: obj.get_dl_from_texts(texts) and returns: DataLoader