text: import torch
  from torch.utils.data import Dataset, DataLoader
  
  
  class KanHope(Dataset):
  
      r"""An abstract class representing a :class:`Dataset`.
  
          All datasets that represent a map from keys to data samples should subclass
          it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
          data sample for a given key. Subclasses could also optionally overwrite
          :meth:`__len__`, which is expected to return the size of the dataset by many
          :class:`~torch.utils.data.Sampler` implementations and the default options
          of :class:`~torch.utils.data.DataLoader`.
  
          .. note::
            :class:`~torch.utils.data.DataLoader` by default constructs a index
            sampler that yields integral indices.  To make it work with a map-style
            dataset with non-integral indices/keys, a custom sampler must be provided.
          """
      def __init__(self, text: object, translation: object, label: object, tokenizer1: object, tokenizer2: object, max_len: object) -> object:
          self.text = text
          self.translation = translation
          self.label = label
          self.tokenizer1 = tokenizer1
          self.tokenizer2 = tokenizer2
          self.max_len = max_len
  
      def __len__(self):
          return len(self.text)
  
      def __getitem__(self, item):
          text = str(self.text[item])
          translation = str(self.translation[item])
          label = self.label[item]
  
          encoding1 = self.tokenizer1.encode_plus(
              text,
              add_special_tokens=True,
              max_length=self.max_len,
              return_token_type_ids=False,
              padding='max_length',
              return_attention_mask=True,
              return_tensors='pt',
              truncation=True
          )
  
          encoding2 = self.tokenizer2.encode_plus(
              translation,
              add_special_tokens=True,
              max_length=self.max_len,
              return_token_type_ids=False,
              padding='max_length',
              return_attention_mask=True,
              return_tensors='pt',
              truncation=True
          )
  
          return {
              'text': text,
              'translation': translation,
              'input_ids1': encoding1['input_ids'].flatten(),
              'input_ids2': encoding2['input_ids'].flatten(),
              'attention_mask1': encoding1['attention_mask'].flatten(),
              'attention_mask2': encoding2['attention_mask'].flatten(),
              'label': torch.tensor(label, dtype=torch.float)
          }
  
  
  