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(
          "--encoder-layers",
          type=int,
          metavar="L",
          help="num encoder layers",
      )
      parser.add_argument(
          "--encoder-embed-dim",
          type=int,
          metavar="H",
          help="encoder embedding dimension",
      )
      parser.add_argument(
          "--encoder-ffn-embed-dim",
          type=int,
          metavar="F",
          help="encoder embedding dimension for FFN",
      )
      parser.add_argument(
          "--encoder-attention-heads",
          type=int,
          metavar="A",
          help="num encoder attention heads",
      )
      parser.add_argument(
          "--activation-fn",
          choices=utils.get_available_activation_fns(),
          help="activation function to use",
      )
      parser.add_argument(
          "--pooler-activation-fn",
          choices=utils.get_available_activation_fns(),
          help="activation function to use for pooler layer",
      )
      parser.add_argument(
          "--encoder-normalize-before",
          action="store_true",
          help="apply layernorm before each encoder block",
      )
      parser.add_argument(
          "--layernorm-embedding",
          action="store_true",
          help="add layernorm to embedding",
      )
      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(
          "--activation-dropout",
          type=float,
          metavar="D",
          help="dropout probability after activation in FFN",
      )
      parser.add_argument(
          "--pooler-dropout",
          type=float,
          metavar="D",
          help="dropout probability in the masked_lm pooler layers",
      )
      parser.add_argument(
          "--max-positions",
          type=int,
          help="number of positional embeddings to learn",
      )
      parser.add_argument(
          "--load-checkpoint-heads",
          action="store_true",
          help="(re-)register and load heads when loading checkpoints",
      )
      parser.add_argument(
          "--untie-weights-roberta",
          action="store_true",
          help="Untie weights between embeddings and classifiers in RoBERTa",
      )
      # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
      parser.add_argument(
          "--encoder-layerdrop",
          type=float,
          metavar="D",
          default=0,
          help="LayerDrop probability for encoder",
      )
      parser.add_argument(
          "--encoder-layers-to-keep",
          default=None,
          help="which layers to *keep* when pruning as a comma-separated list",
      )
      # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
      parser.add_argument(
          "--quant-noise-pq",
          type=float,
          metavar="D",
          default=0,
          help="iterative PQ quantization noise at training time",
      )
      parser.add_argument(
          "--quant-noise-pq-block-size",
          type=int,
          metavar="D",
          default=8,
          help="block size of quantization noise at training time",
      )
      parser.add_argument(
          "--quant-noise-scalar",
          type=float,
          metavar="D",
          default=0,
          help="scalar quantization noise and scalar quantization at training time",
      )
      # args for "Better Fine-Tuning by Reducing Representational Collapse" (Aghajanyan et al. 2020)
      parser.add_argument(
          "--spectral-norm-classification-head",
          action="store_true",
          default=False,
          help="Apply spectral normalization on the classification head",
      )
      # args for Fully Sharded Data Parallel (FSDP) training
      parser.add_argument(
          "--min-params-to-wrap",
          type=int,
          metavar="D",
          default=DEFAULT_MIN_PARAMS_TO_WRAP,
          help=(
              "minimum number of params for a layer to be wrapped with FSDP() when "
              "training with --ddp-backend=fully_sharded. Smaller values will "
              "improve memory efficiency, but may make torch.distributed "
              "communication less efficient due to smaller input sizes. This option "
              "is set to 0 (i.e., always wrap) when --checkpoint-activations or "
              "--offload-activations are passed."
          ),
      )
      # args for AdaPruning
      # In short, it adds regularizarion for the multihead attention module and feed forward neural nets
      # For more details, please refer to the paper https://openreview.net/forum?id=_CMSV7FTzGI
      parser.add_argument(
          "--mha-reg-scale-factor",
          type=float,
          metavar="D",
          default=0.0,
          help="scaling factor for regularization term in adptive pruning, recommendation is 0.000375",
      )
      parser.add_argument(
          "--ffn-reg-scale-factor",
          type=float,
          metavar="D",
          default=0.0,
          help="scaling factor for regularization term in adptive pruning, recommendation is 0.000375",
      )
      parser.add_argument(
          "--mha-heads-to-keep",
          type=int,
          metavar="D",
          default=-1,
          help="number of heads to keep in each multi-head attention module, -1 means keeping all heads",
      )
      parser.add_argument(
          "--ffn-blocks-to-remove",
          type=int,
          metavar="D",
          default=-1,
          help="number of feedforward blocks to remove in each transformer layer, -1 means keeping all ffn blocks",
      )
  
imports: import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder
  from fairseq.modules import LayerNorm
  from fairseq.modules.quant_noise import quant_noise
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import safe_getattr, safe_hasattr
  from .hub_interface import RobertaHubInterface
label_desc: Use image node_id 3 for calling the RobertaModel obj's underlying member method code with example usage: obj.add_args(parser) without return types