def __call__(self, examples: List[Dict[(str, torch.Tensor)]]) -> Dict[(str, torch.Tensor)]:
    input_ids = [example['input_ids'] for example in examples]
    input_ids = _collate_batch(input_ids, self.tokenizer)
    (input_ids, labels, attention_mask) = self.mask_tokens(input_ids)
    token_type_ids = [example['token_type_ids'] for example in examples]
    token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)
    sop_label_list = [example['sentence_order_label'] for example in examples]
    sentence_order_label = torch.stack(sop_label_list)
    return {'input_ids': input_ids, 'labels': labels, 'attention_mask': attention_mask, 'token_type_ids': token_type_ids, 'sentence_order_label': sentence_order_label}
def __call__(self, examples: List[Dict[(str, torch.Tensor)]]) -> Dict[(str, torch.Tensor)]:
    input_ids = [example['input_ids'] for example in examples]
    input_ids = _collate_batch(input_ids, self.tokenizer)
    (input_ids, labels, attention_mask) = self.mask_tokens(input_ids)
    token_type_ids = [example['token_type_ids'] for example in examples]
    token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)
    sop_label_list = [example['sentence_order_label'] for example in examples]
    sentence_order_label = torch.stack(sop_label_list)
    return {'input_ids': input_ids, 'labels': labels, 'attention_mask': attention_mask, 'token_type_ids': token_type_ids, 'sentence_order_label': sentence_order_label}