code: def forward(
      self,
      src_tokens,
      prev_output_tokens,
      features_only=False,
      **kwargs,
  ):
      encoder_out = self.encoder(src_tokens)
      decoder_out = self.decoder(
          prev_output_tokens, encoder_out, features_only=features_only
      )
      return decoder_out
  
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 7 for calling the LSTransformerModel obj's underlying member method code with example usage: obj.forward(src_tokens, prev_output_tokens, features_only) and returns: decoder_out