code: def decode(self, inputs, caches):
      tgt_ids = inputs["tgt_ids"]
      tgt_pos = inputs["tgt_pos"]
      tgt_generation_mask = inputs["tgt_generation_mask"]
      predictions = tgt_ids
  
      # TODO
      step = 0
      while step < self.max_dec_len:
          # [-1, 1]
          append_mask = paddle.cast(
              tgt_ids != self.eos_id, dtype=tgt_generation_mask.dtype
          )
          tgt_generation_mask = paddle.concat(
              [tgt_generation_mask, paddle.unsqueeze(append_mask, 1)],
              axis=-1,
          )
          tgt_sent = paddle.ones(
              [tgt_generation_mask.shape[0], 1], dtype=tgt_ids.dtype
          )
  
          # [-1, 1, hidden_size]
          out, caches = self.plato2_encoder(
              caches, tgt_ids, tgt_sent, tgt_pos, tgt_generation_mask
          )
          out = paddle.squeeze(out, axis=1)
  
          # [-1, hidden_size]
          trans = self.logits_fc_layer(out)
          trans = self.gelu_layer(trans)
          trans = self.logits_layer_norm(trans)
  
          # [-1, vocab_size]
          logits = (
              paddle.matmul(
                  trans,
                  self.plato2_encoder.word_embedding_layer.weight,
                  transpose_y=True,
              )
              + self.logits_bias
          )
          logits[:, self.unk_id] = -1e9
          logits[:, self.bos_id] = -1e9
          logits[:, self.mask_id] = -1e9
          if step < self.min_dec_len:
              logits[:, self.eos_id] = -1e9
          logits = (
              logits * append_mask + (1 - append_mask) * self.after_eos
          )
          probs = self.softmax(logits)
  
          # [-1, topk]
          topk_probs, _ = paddle.topk(probs, k=self.topk)
          mask = paddle.cast(probs >= topk_probs[:, -1:], "float32")
          sums = paddle.sum(topk_probs, axis=-1, keepdim=True)
          new_probs = probs * mask / sums
          # [-1, 1]
          sampling_ids = paddle.multinomial(new_probs)
  
          step = step + 1
          tgt_ids = sampling_ids
          tgt_pos = tgt_pos + 1
          predictions = paddle.concat([predictions, tgt_ids], axis=1)
      return predictions
  
imports: from collections import namedtuple
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 3 for calling the Plato2InferModel obj's underlying member method code with example usage: obj.decode(inputs, caches) and returns: predictions