code: def __init__(
      self,
      max_seq_len=512,
      pad_to_max_seq_len=True,
      return_attention_mask=True,
      return_token_type_ids=True,
      truncation_strategy="longest_first",
      return_overflowing_tokens=False,
      return_special_tokens_mask=False,
      infer_mode=False,
      **kwargs,
  ):
      self.max_seq_len = max_seq_len
      self.pad_to_max_seq_len = max_seq_len
      self.return_attention_mask = return_attention_mask
      self.return_token_type_ids = return_token_type_ids
      self.truncation_strategy = truncation_strategy
      self.return_overflowing_tokens = return_overflowing_tokens
      self.return_special_tokens_mask = return_special_tokens_mask
      self.pad_token_label_id = (
          paddle.nn.CrossEntropyLoss().ignore_index
      )
      self.infer_mode = infer_mode
  
imports: import paddle
  import numpy
label_desc: Use image node_id 1 to create a new VQATokenPad object from inherited base classes: object with example: obj = VQATokenPad(max_seq_len, pad_to_max_seq_len, return_attention_mask, return_token_type_ids, truncation_strategy, return_overflowing_tokens, return_special_tokens_mask, infer_mode)