class_docstr: embedder + encoder
class_docstr_tok: ["embedder","+","encoder"]
code: def __init__(
      self,
      src_vocab_size,
      max_length,
      n_layer,
      n_head,
      d_key,
      d_value,
      d_model,
      d_inner_hid,
      prepostprocess_dropout,
      attention_dropout,
      relu_dropout,
      preprocess_cmd,
      postprocess_cmd,
      weight_sharing,
      bos_idx=0,
  ):
      super(WrapEncoder, self).__init__()
  
      self.prepare_decoder = PrepareDecoder(
          src_vocab_size,
          d_model,
          max_length,
          prepostprocess_dropout,
          bos_idx=bos_idx,
      )
      self.encoder = Encoder(
          n_layer,
          n_head,
          d_key,
          d_value,
          d_model,
          d_inner_hid,
          prepostprocess_dropout,
          attention_dropout,
          relu_dropout,
          preprocess_cmd,
          postprocess_cmd,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import ParamAttr, nn
  from paddle import nn, ParamAttr
  from paddle.nn import functional
  import numpy
label_desc: Use image node_id 1 to create a new WrapEncoder object from inherited base classes: nn with example: obj = WrapEncoder(src_vocab_size, max_length, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing, bos_idx)