class_docstr: Train Transformer for LASER task
  
  Requires --task laser
class_docstr_tok: ["Train","Transformer","for","LASER","task","Requires","--","task","laser"]
docstr: Add model-specific arguments to the parser.
docstr_tok: ["Add","model-specific","arguments","to","the","parser","."]
code: def add_args(parser):
      """Add model-specific arguments to the parser."""
      TransformerModel.add_args(parser)
      parser.add_argument(
          "--decoder-lang-embed-dim",
          type=int,
          metavar="N",
          help="decoder language embedding dimension",
      )
  
imports: import logging
  from typing import Any, Dict, List, Optional
  from torch import Tensor
  import torch
  import torch.nn
  from fairseq.models import FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import base_architecture, Embedding, TransformerModel, TransformerEncoder, TransformerDecoder
  from fairseq.modules import TransformerDecoderLayer
label_desc: Use image node_id 3 for calling the LaserTransformerModel obj's underlying member method code with example usage: obj.add_args(parser) without return types