code: def build_decoder_layer(self, config):
      dec_config = LSTransformerDecoderLayer.get_config(
          max_batch_tokens=config.max_batch_tokens,
          max_seq_len=config.max_seq_len,
          hidden_size=config.hidden_size,
          intermediate_size=config.intermediate_size,
          nhead=config.nhead,
          attn_prob_dropout_ratio=config.attn_prob_dropout_ratio,
          activation_dropout_ratio=config.activation_dropout_ratio,
          hidden_dropout_ratio=config.hidden_dropout_ratio,
          pre_layer_norm=config.pre_layer_norm,
          activation_fn=config.activation_fn,
          fp16=config.fp16,
          local_rank=config.local_rank,
          nlayer=config.num_decoder_layer,
      )
      return LSTransformerDecoderLayer(dec_config)
  
imports: from dataclasses import dataclass
  import torch
  import torch.nn
  from lightseq.training import LSTransformerEmbeddingLayer, LSTransformerEncoderLayer, LSTransformerDecoderLayer
  from lightseq.training.ops.pytorch.util import MODEL_ARCH
  from lightseq.training.ops.pytorch.quantization import QuantLinear
label_desc: Use image node_id 2 for calling the LSTransformerDecoder obj's underlying member method code with example usage: obj.build_decoder_layer(config) and returns: LSTransformerDecoderLayer