class_docstr: Tokenize
      
code: def __getitem__(self, item):
      value = self.data[item]
      tokens = value["tokens"]
      text = " ".join(tokens)
      encoded = self.tokenizer.encode_plus(
          text=text,
          add_special_tokens=True,
          max_length=self.max_sequence_len,
          truncation=True,
          padding="max_length",
          return_attention_mask=True,
          return_tensors="pt",
      )
      encoded["labels"] = torch.LongTensor(
          self._pad_labels(value["labels"])
      )
      return encoded
  
imports: import logging
  from os.path import join
  import pytorch_lightning
  from pytorch_lightning.loggers import WandbLogger
  from torch.optim import AdamW
  import torch.nn
  import torch
  from torchmetrics import F1
  from torch.utils.data import DataLoader, Dataset
  from transformers import AutoTokenizer, AutoModel
label_desc: Use image node_id 4 for calling the TokenClassificationDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: encoded