code: def __getitem__(self, item):
      review = str(self.review[item])
      inputs = self.tokenizer.encode_plus(
          review,
          None,
          add_special_tokens=True,
          max_length=self.max_len,
          padding="max_length",
          truncation=True,
      )
  
      ids = inputs["input_ids"]
      mask = inputs["attention_mask"]
      token_type_ids = inputs["token_type_ids"]
  
      return {
          "ids": torch.tensor(ids, dtype=torch.long),
          "mask": torch.tensor(mask, dtype=torch.long),
          "token_type_ids": torch.tensor(
              token_type_ids, dtype=torch.long
          ),
          "targets": torch.tensor(self.target[item], dtype=torch.float),
      }
  
file_path: tez/examples/nlp/imdb_sentiment_classification.py
imports: import pandas
  import torch
  import torch.nn
  from sklearn import metrics, model_selection
  from transformers import AutoConfig, AutoModel, AutoTokenizer, get_linear_schedule_with_warmup
  from tez import Tez, TezConfig
  from tez.callbacks import EarlyStopping
label_desc: Use image node_id 3 for calling the IMDBDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: dict