code: def forward(self, inputs):
      token_ids = inputs["token_ids"]
      type_ids = inputs["type_ids"]
      pos_ids = inputs["pos_ids"]
      generation_mask = inputs["generation_mask"]
      latent_id = inputs["latent_id"]
      data_id = inputs["data_id"]
  
      # [-1, 1, latent_type_size]
      latent_id = F.one_hot(latent_id, self.latent_type_size)
      # [-1, 1, hidden_size]
      latent_emb = paddle.matmul(
          latent_id, self.latent_weight, transpose_y=True
      )
  
      caches = self.plato2_encoder.gen_caches(token_ids)
  
      # [-1, seq_len + 1, hidden_size]
      enc_out, new_caches = self.plato2_encoder(
          caches,
          token_ids,
          type_ids,
          pos_ids,
          generation_mask,
          latent_emb,
      )
  
      pred_ids = self.decode(inputs, new_caches)
  
      nsp_inputs = self.gen_nsp_input(token_ids, pred_ids)
      # [-1, 2]
      probs = self.nsp_predictor(nsp_inputs)
  
      return self.get_results(data_id, token_ids, pred_ids, probs)
  
imports: from collections import namedtuple
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the Plato2InferModel obj's underlying member method code with example usage: obj.forward(inputs) and returns: self