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."""
      parser.add_argument(
          "--dropout",
          default=0.1,
          type=float,
          metavar="D",
          help="dropout probability",
      )
      parser.add_argument(
          "--attention-dropout",
          default=0.0,
          type=float,
          metavar="D",
          help="dropout probability for attention weights",
      )
      parser.add_argument(
          "--relu-dropout",
          default=0.0,
          type=float,
          metavar="D",
          help="dropout probability after ReLU in FFN",
      )
      parser.add_argument(
          "--input-dropout",
          type=float,
          metavar="D",
          help="dropout probability of the inputs",
      )
      parser.add_argument(
          "--decoder-embed-dim",
          type=int,
          metavar="N",
          help="decoder embedding dimension",
      )
      parser.add_argument(
          "--decoder-output-dim",
          type=int,
          metavar="N",
          help="decoder output dimension",
      )
      parser.add_argument(
          "--decoder-input-dim",
          type=int,
          metavar="N",
          help="decoder input dimension",
      )
      parser.add_argument(
          "--decoder-ffn-embed-dim",
          type=int,
          metavar="N",
          help="decoder embedding dimension for FFN",
      )
      parser.add_argument(
          "--decoder-layers",
          type=int,
          metavar="N",
          help="num decoder layers",
      )
      parser.add_argument(
          "--decoder-attention-heads",
          type=int,
          metavar="N",
          help="num decoder attention heads or LightConv/DynamicConv heads",
      )
      parser.add_argument(
          "--decoder-normalize-before",
          default=False,
          action="store_true",
          help="apply layernorm before each decoder block",
      )
      parser.add_argument(
          "--adaptive-softmax-cutoff",
          metavar="EXPR",
          help="comma separated list of adaptive softmax cutoff points. "
          "Must be used with adaptive_loss criterion",
      )
      parser.add_argument(
          "--adaptive-softmax-dropout",
          type=float,
          metavar="D",
          help="sets adaptive softmax dropout for the tail projections",
      )
      parser.add_argument(
          "--adaptive-softmax-factor",
          type=float,
          metavar="N",
          help="adaptive input factor",
      )
      parser.add_argument(
          "--no-token-positional-embeddings",
          default=False,
          action="store_true",
          help="if set, disables positional embeddings (outside self attention)",
      )
      parser.add_argument(
          "--share-decoder-input-output-embed",
          default=False,
          action="store_true",
          help="share decoder input and output embeddings",
      )
      parser.add_argument(
          "--character-embeddings",
          default=False,
          action="store_true",
          help="if set, uses character embedding convolutions to produce token embeddings",
      )
      parser.add_argument(
          "--character-filters",
          type=str,
          metavar="LIST",
          default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]",
          help="size of character embeddings",
      )
      parser.add_argument(
          "--character-embedding-dim",
          type=int,
          metavar="N",
          default=4,
          help="size of character embeddings",
      )
      parser.add_argument(
          "--char-embedder-highway-layers",
          type=int,
          metavar="N",
          default=2,
          help="number of highway layers for character token embeddder",
      )
      parser.add_argument(
          "--adaptive-input",
          default=False,
          action="store_true",
          help="if set, uses adaptive input",
      )
      parser.add_argument(
          "--adaptive-input-factor",
          type=float,
          metavar="N",
          help="adaptive input factor",
      )
      parser.add_argument(
          "--adaptive-input-cutoff",
          metavar="EXPR",
          help="comma separated list of adaptive input cutoff points.",
      )
      parser.add_argument(
          "--tie-adaptive-weights",
          action="store_true",
          help="if set, ties the weights of adaptive softmax and adaptive input",
      )
      parser.add_argument(
          "--tie-adaptive-proj",
          action="store_true",
          help="if set, ties the projection weights of adaptive softmax and adaptive input",
      )
      parser.add_argument(
          "--decoder-learned-pos",
          action="store_true",
          help="use learned positional embeddings in the decoder",
      )
  
      """LightConv and DynamicConv arguments"""
      parser.add_argument(
          "--decoder-kernel-size-list",
          type=lambda x: utils.eval_str_list(x, int),
          help='list of kernel size (default: "[3,7,15,31,31,31]")',
      )
      parser.add_argument(
          "--decoder-glu",
          type=utils.eval_bool,
          help="glu after in proj",
      )
      parser.add_argument(
          "--decoder-conv-type",
          default="dynamic",
          type=str,
          choices=["dynamic", "lightweight"],
          help="type of convolution",
      )
      parser.add_argument(
          "--weight-softmax", default=True, type=utils.eval_bool
      )
      parser.add_argument(
          "--weight-dropout",
          type=float,
          metavar="D",
          help="dropout probability for conv weights",
      )
  
file_path: fairseq/fairseq/models/lightconv_lm.py
imports: from fairseq import utils
  from fairseq.models import FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.lightconv import Embedding, LightConvDecoder
  from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder
label_desc: Use image node_id 2 for calling the LightConvLanguageModel obj's underlying member method code with example usage: obj.add_args(parser) without return types