code: def build_embedding(self, config):
      emb_config = LSTransformerEmbeddingLayer.get_config(
          vocab_size=config.vocab_size,
          embedding_dim=config.hidden_size,
          max_batch_tokens=config.max_batch_tokens,
          max_seq_len=config.max_seq_len,
          padding_idx=config.padding_idx,
          dropout=config.hidden_dropout_ratio,
          fp16=config.fp16,
          local_rank=config.local_rank,
      )
      emb = LSTransformerEmbeddingLayer(emb_config)
      return emb
  
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 4 for calling the LSTransformer obj's underlying member method code with example usage: obj.build_embedding(config) and returns: emb