code: def forward_embedding(
      self, prev_output_tokens, incremental_state=None
  ):
      step = 0
      if incremental_state is not None:
          step = prev_output_tokens.size(1) - 1
          prev_output_tokens = prev_output_tokens[:, -1:]
  
      x = self.embed_tokens(prev_output_tokens, step)
      return x, prev_output_tokens
  
imports: import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.modules import LayerNorm
  from lightseq.training.ops.pytorch.layer_base import TransformerEmbeddingLayerBase
  from lightseq.training.ops.pytorch.quantization import QuantLinear, disable_quant, enable_quant, ptq_mode, qat_mode
label_desc: Use image node_id 3 for calling the LSTransformerDecoder obj's underlying member method code with example usage: obj.forward_embedding(prev_output_tokens, incremental_state) and returns: x,  prev_output_tokens