class_docstr: The layer to compose the transformer decoder.
  
  Args:
      d_model(int): The input feature's channels.
      nhead(int): the number of head for MHSA.
      dim_feedforward(int): The internal channels of linear layer.
      dropout(int): the dropout probability.
      activation(str): the kind of activation that used.
class_docstr_tok: ["The","layer","to","compose","the","transformer","decoder",".","Args",":","d_model","(","int",")",":","The","input","feature","'s","channels",".","nhead","(","int",")",":","the","number","of","head","for","MHSA",".","dim_feedforward","(","int",")",":","The","internal","channels","of","linear","layer",".","dropout","(","int",")",":","the","dropout","probability",".","activation","(","str",")",":","the","kind","of","activation","that","used","."]
code: def init_weight(self):
      param_init.multihead_fill(self.self_attn, True)
      param_init.multihead_fill(self.multihead_attn, True)
      param_init.th_linear_fill(self.linear1)
      param_init.th_linear_fill(self.linear2)
  
imports: import math
  import copy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.utils import utils
  from paddleseg.core.train import check_logits_losses
label_desc: Use image node_id 2 for calling the DecoderLayer obj's underlying member method code with example usage: obj.init_weight() without return types