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(
          "--input-feat-per-channel",
          type=int,
          metavar="N",
          help="encoder input dimension per input channel",
      )
      parser.add_argument(
          "--vggblock-enc-config",
          type=str,
          metavar="EXPR",
          help="""
      an array of tuples each containing the configuration of one vggblock
      [(out_channels, conv_kernel_size, pooling_kernel_size,num_conv_layers), ...]
      """,
      )
      parser.add_argument(
          "--transformer-enc-config",
          type=str,
          metavar="EXPR",
          help="""
      a tuple containing the configuration of the Transformer layers
      configurations:
      [(input_dim,
        num_heads,
        ffn_dim,
        normalize_before,
        dropout,
        attention_dropout,
        relu_dropout), ]""",
      )
      parser.add_argument(
          "--enc-output-dim",
          type=int,
          metavar="N",
          help="encoder output dimension, projecting the LSTM output",
      )
      parser.add_argument(
          "--in-channels",
          type=int,
          metavar="N",
          help="number of encoder input channels",
      )
      parser.add_argument(
          "--transformer-context",
          type=str,
          metavar="EXPR",
          help="""
      either None or a tuple of two ints, indicating left/right context a
      transformer can have access to""",
      )
      parser.add_argument(
          "--transformer-sampling",
          type=str,
          metavar="EXPR",
          help="""
      either None or a tuple of ints, indicating sampling factor in each layer""",
      )
  
imports: import argparse
  import math
  from collections.abc import Iterable
  import torch
  import torch.nn
  from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LinearizedConvolution, TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock
label_desc: Use image node_id 2 for calling the VGGTransformerEncoderModel obj's underlying member method code with example usage: obj.add_args(parser) without return types