code: def smart_batching_collate(self, batch):
      texts = [[] for _ in range(len(batch[0].texts))]
      labels = []
  
      for example in batch:
          for idx, text in enumerate(example.texts):
              texts[idx].append(text.strip())
  
          labels.append(example.label)
  
      tokenized = self.tokenizer(
          *texts,
          padding=True,
          truncation="longest_first",
          return_tensors="pt",
          max_length=self.max_length,
      )
      labels = torch.tensor(
          labels,
          dtype=torch.float
          if self.config.num_labels == 1
          else torch.long,
      ).to(self._target_device)
  
      for name in tokenized:
          tokenized[name] = tokenized[name].to(self._target_device)
  
      return tokenized, labels
  
imports: from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
  import numpy
  import logging
  import os
  from typing import Dict, Type, Callable, List
  import torch
  from torch import nn
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from tqdm.autonotebook import tqdm, trange
  from ..None import SentenceTransformer, util
  from ..evaluation import SentenceEvaluator
label_desc: Use image node_id 2 for calling the CrossEncoder obj's underlying member method code with example usage: obj.smart_batching_collate(batch) and returns: tokenized,  labels