def _get_decoder(self, units, vocab_size, embed, prefix):
with self.name_scope():<EOL><INDENT>decoder = nn.HybridSequential(prefix=prefix)<EOL>decoder.add(nn.Dense(units, flatten=False))<EOL>decoder.add(GELU())<EOL>decoder.add(BERTLayerNorm(in_channels=units))<EOL>decoder.add(nn.Dense(vocab_size, flatten=False, params=embed.collect_params()))<EOL><DEDENT>assert decoder[<NUM_LIT:3>].weight == list(embed.collect_params().values())[<NUM_LIT:0>],'<STR_LIT>'<EOL>return decoder<EOL>
Construct a decoder for the masked language model task