code: def __init__(
      self,
      nsp_reader,
      num_layers,
      n_head,
      hidden_size,
      vocab_size=8001,
      type_size=2,
      latent_type_size=20,
      max_position_seq_len=256,
      act_dropout=0.1,
      attn_dropout=0.1,
      max_dec_len=64,
      min_dec_len=1,
      topk=10,
  ):
      super(Plato2InferModel, self).__init__()
  
      self.nsp_reader = nsp_reader
      self.num_layers = num_layers
      self.latent_type_size = latent_type_size
      self.max_dec_len = max_dec_len
      self.min_dec_len = min_dec_len
      self.topk = topk
      self.unk_id = 0
      self.bos_id = 1
      self.eos_id = 2
      self.mask_id = 8000
      self.after_eos = paddle.ones([vocab_size]) * -1e9
      self.after_eos[self.eos_id] = 0
      self.is_cn = False
      self.batch_size = 1
  
      self.latent_weight = paddle.create_parameter(
          [hidden_size, latent_type_size], "float32"
      )
  
      self.plato2_encoder = Plato2Encoder(
          vocab_size,
          type_size,
          max_position_seq_len,
          num_layers,
          n_head,
          hidden_size,
          attn_dropout,
          act_dropout,
      )
  
      self.logits_fc_layer = nn.Linear(hidden_size, hidden_size)
      self.logits_layer_norm = nn.LayerNorm(hidden_size)
      self.logits_bias = paddle.create_parameter(
          [vocab_size], "float32", is_bias=True
      )
  
      self.nsp_predictor = NSP(
          vocab_size,
          type_size,
          max_position_seq_len,
          num_layers,
          n_head,
          hidden_size,
          attn_dropout,
          act_dropout,
      )
  
      self.gelu_layer = nn.GELU()
      self.softmax = nn.Softmax()
  
imports: from collections import namedtuple
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new Plato2InferModel object from inherited base classes: nn with example: obj = Plato2InferModel(nsp_reader, num_layers, n_head, hidden_size, vocab_size, type_size, latent_type_size, max_position_seq_len, act_dropout, attn_dropout, max_dec_len, min_dec_len, topk)