code: def __getitem__(self, index: int):
      data_indexed = self.data[index]
      sentence = data_indexed["text"].replace("\n", "").replace(".", "")
      word_segmented = word_tokenize(
          sentence, "text"
      )  # PhoBert requires word_segmented text
      encoded = self.tokenizer.encode_plus(
          text=word_segmented,
          add_special_tokens=True,
          max_length=self.max_sequence_len,
          truncation=True,
          padding="max_length",
          return_attention_mask=True,
          return_tensors="pt",
      )
      hot_encoding = []
      for i in range(self.num_labels):
          if i in data_indexed["label_ids"]:
              hot_encoding.append(1.0)
          else:
              hot_encoding.append(0.0)
      label_hot_encoding = torch.FloatTensor(hot_encoding)
      output = dict(
          input_ids=encoded["input_ids"],
          attention_mask=encoded["attention_mask"],
          labels=label_hot_encoding,
      )
      return output
  
imports: import torch
  import hydra
  import torch.nn
  import pytorch_lightning
  from omegaconf import DictConfig
  from torchmetrics import F1Score
  from torch.utils.data import Dataset, DataLoader
  from transformers import AutoTokenizer, AdamW, RobertaModel, BertPreTrainedModel, RobertaConfig
  from underthesea import word_tokenize
  from underthesea.datasets.uit_absa_hotel.uit_absa_hotel import UITABSAHotel
label_desc: Use image node_id 3 for calling the UITABSADataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: output