code: def forward_embedding(self, trg_tokens, cache=None):
      step = 0
      if cache is not None:
          step = trg_tokens.size(1) - 1
          trg_tokens = trg_tokens[:, -1:]
      x = self.embed_tokens(trg_tokens, step)
      return x
  
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 3 for calling the LSTransformerDecoder obj's underlying member method code with example usage: obj.forward_embedding(trg_tokens, cache) and returns: x