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",
          type=float,
          metavar="D",
          help="dropout probability",
      )
      parser.add_argument(
          "--attention-dropout",
          type=float,
          metavar="D",
          help="dropout probability for attention weights",
      )
      parser.add_argument(
          "--relu-dropout",
          type=float,
          metavar="D",
          help="dropout probability after ReLU in FFN",
      )
      parser.add_argument(
          "--encoder-pretrained-embed",
          type=str,
          metavar="STR",
          help="path to pre-trained encoder embedding",
      )
      parser.add_argument(
          "--encoder-embed-dim",
          type=int,
          metavar="N",
          help="encoder embedding dimension",
      )
      parser.add_argument(
          "--encoder-ffn-embed-dim",
          type=int,
          metavar="N",
          help="encoder embedding dimension for FFN",
      )
      parser.add_argument(
          "--encoder-freeze-embed",
          default=False,
          action="store_true",
          help=(
              "whether to freeze the encoder embedding or allow it to be "
              "updated during training"
          ),
      )
      parser.add_argument(
          "--encoder-layers",
          type=int,
          metavar="N",
          help="num encoder layers",
      )
      parser.add_argument(
          "--encoder-attention-heads",
          type=int,
          metavar="N",
          help="num encoder attention heads",
      )
      parser.add_argument(
          "--encoder-normalize-before",
          default=False,
          action="store_true",
          help="apply layernorm before each encoder block",
      )
      parser.add_argument(
          "--encoder-learned-pos",
          default=False,
          action="store_true",
          help="use learned positional embeddings in the encoder",
      )
      parser.add_argument(
          "--decoder-pretrained-embed",
          type=str,
          metavar="STR",
          help="path to pre-trained decoder embedding",
      )
      parser.add_argument(
          "--decoder-embed-dim",
          type=int,
          metavar="N",
          help="decoder embedding dimension",
      )
      parser.add_argument(
          "--decoder-freeze-embed",
          default=False,
          action="store_true",
          help=(
              "whether to freeze the encoder embedding or allow it to be "
              "updated during training"
          ),
      )
      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",
      )
      parser.add_argument(
          "--decoder-reduced-attention-dim",
          type=int,
          default=None,
          metavar="N",
          help="if specified, computes attention with this dimensionality "
          "(instead of using encoder output dims)",
      )
      parser.add_argument(
          "--decoder-lstm-units",
          type=int,
          metavar="N",
          help="num LSTM units for each decoder layer",
      )
      parser.add_argument(
          "--decoder-out-embed-dim",
          default=None,
          type=int,
          metavar="N",
          help="decoder output embedding dimension",
      )
  
      # Args for vocab reduction
      vocab_reduction.add_args(parser)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import MultiheadAttention
  from pytorch_translate import multilingual_model, transformer, vocab_reduction
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 2 for calling the HybridTransformerRNNModel obj's underlying member method code with example usage: obj.add_args(parser) without return types