code: def __getitem__(self, item):
      text = self.texts[item]
  
      encoding = self.tokenizer(
          text,
          return_tensors="pt",
          padding="max_length",
          max_length=self.max_token_length,
      )
      input_ids = encoding["input_ids"]
      hot_encoding = []
      for i in range(self.num_labels):
          if i in self.labels[item]:
              hot_encoding.append(1.0)
          else:
              hot_encoding.append(0.0)
      label_hot_encoding = torch.FloatTensor(hot_encoding)
      output = dict(
          text=text, label=label_hot_encoding, input_ids=input_ids
      )
      return output
  
file_path: underthesea/examples/sentiment/data.py
imports: import torch
  from torch.utils.data import Dataset, DataLoader
  import pytorch_lightning
label_desc: Use image node_id 3 for calling the MultiLabelClassificationDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: output