code: def __call__(self, data):
      needs_to_be_padded = (
          self.pad_to_max_seq_len
          and len(data["input_ids"]) < self.max_seq_len
      )
  
      if needs_to_be_padded:
          if "tokenizer_params" in data:
              tokenizer_params = data.pop("tokenizer_params")
          else:
              tokenizer_params = dict(
                  padding_side="right",
                  pad_token_type_id=0,
                  pad_token_id=1,
              )
  
          difference = self.max_seq_len - len(data["input_ids"])
          if tokenizer_params["padding_side"] == "right":
              if self.return_attention_mask:
                  data["attention_mask"] = [1] * len(
                      data["input_ids"]
                  ) + [0] * difference
              if self.return_token_type_ids:
                  data["token_type_ids"] = (
                      data["token_type_ids"]
                      + [tokenizer_params["pad_token_type_id"]]
                      * difference
                  )
              if self.return_special_tokens_mask:
                  data["special_tokens_mask"] = (
                      data["special_tokens_mask"] + [1] * difference
                  )
              data["input_ids"] = (
                  data["input_ids"]
                  + [tokenizer_params["pad_token_id"]] * difference
              )
              if not self.infer_mode:
                  data["labels"] = (
                      data["labels"]
                      + [self.pad_token_label_id] * difference
                  )
              data["bbox"] = data["bbox"] + [[0, 0, 0, 0]] * difference
          elif tokenizer_params["padding_side"] == "left":
              if self.return_attention_mask:
                  data["attention_mask"] = [0] * difference + [1] * len(
                      data["input_ids"]
                  )
              if self.return_token_type_ids:
                  data["token_type_ids"] = [
                      tokenizer_params["pad_token_type_id"]
                  ] * difference + data["token_type_ids"]
              if self.return_special_tokens_mask:
                  data["special_tokens_mask"] = [1] * difference + data[
                      "special_tokens_mask"
                  ]
              data["input_ids"] = [
                  tokenizer_params["pad_token_id"]
              ] * difference + data["input_ids"]
              if not self.infer_mode:
                  data["labels"] = [
                      self.pad_token_label_id
                  ] * difference + data["labels"]
              data["bbox"] = [[0, 0, 0, 0]] * difference + data["bbox"]
      else:
          if self.return_attention_mask:
              data["attention_mask"] = [1] * len(data["input_ids"])
  
      for key in data:
          if key in [
              "input_ids",
              "labels",
              "token_type_ids",
              "bbox",
              "attention_mask",
          ]:
              if self.infer_mode:
                  if key != "labels":
                      length = min(len(data[key]), self.max_seq_len)
                      data[key] = data[key][:length]
                  else:
                      continue
              data[key] = np.array(data[key], dtype="int64")
      return data
  
imports: import paddle
  import numpy
label_desc: Use image node_id 2 for calling the VQATokenPad obj's underlying member method code with example usage: obj.__call__(data) and returns: data