code: def __getitem__(self, item):
      text = str(self.data[item][self.text_column])
      target = self.data[item][self.target_column]
      target = int(target)
      inputs = self.tokenizer(
          text,
          max_length=self.config.max_seq_length,
          padding="max_length",
          truncation=True,
      )
  
      ids = inputs["input_ids"]
      mask = inputs["attention_mask"]
  
      if "token_type_ids" in inputs:
          token_type_ids = inputs["token_type_ids"]
      else:
          token_type_ids = None
  
      if token_type_ids is not None:
          return {
              "input_ids": torch.tensor(ids, dtype=torch.long),
              "attention_mask": torch.tensor(mask, dtype=torch.long),
              "token_type_ids": torch.tensor(
                  token_type_ids, dtype=torch.long
              ),
              "labels": torch.tensor(target, dtype=torch.long),
          }
      return {
          "input_ids": torch.tensor(ids, dtype=torch.long),
          "attention_mask": torch.tensor(mask, dtype=torch.long),
          "labels": torch.tensor(target, dtype=torch.long),
      }
  
imports: import torch
label_desc: Use image node_id 3 for calling the TextClassificationDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: dict, dict