code: def __init__(
      self, config, tokenizer, set, max_pred_num=20, masked_prob=0.2
  ):
      super(UnilmCollate, self).__init__(config, tokenizer, set)
      self.mask_word_id, self.pad_id = tokenizer.convert_tokens_to_ids(
          ["[MASK]", "[PAD]"]
      )
      self.max_tgt_len = config["tgt_len"]
      self.max_src_len = config["src_len"]
      self.max_len = self.max_src_len + self.max_tgt_len
      if self.max_len > tokenizer.model_max_length:
          self.max_len = tokenizer.model_max_length
          self.max_tgt_len = self.max_len // 4
          self.max_src_len = self.max_len - self.max_tgt_len
      self._tril_matrix = torch.tril(
          torch.ones((self.max_len, self.max_len), dtype=torch.long)
      )
      self.vocab_size = tokenizer.vocab_size
      self.max_pred_num = max_pred_num
      self.masked_prob = masked_prob
  
imports: from textbox.data.abstract_dataset import AbstractCollate, _pad_sequence
  import random
  import torch
label_desc: Use image node_id 1 to create a new UnilmCollate object from inherited base classes: AbstractCollate with example: obj = UnilmCollate(config, tokenizer, set, max_pred_num, masked_prob)