class_docstr: Transformers with convolutional context for ASR
  https://arxiv.org/abs/1904.11660
class_docstr_tok: ["Transformers","with","convolutional","context","for","ASR","https",":","\/\/arxiv.org\/abs\/1904.11660"]
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,
        use_layer_norm), ...])
              """,
      )
      parser.add_argument(
          "--transformer-enc-config",
          type=str,
          metavar="EXPR",
          help=""""
      a tuple containing the configuration of the encoder 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, can be None. If specified, projecting the
      transformer output to the specified dimension""",
      )
      parser.add_argument(
          "--in-channels",
          type=int,
          metavar="N",
          help="number of encoder input channels",
      )
      parser.add_argument(
          "--tgt-embed-dim",
          type=int,
          metavar="N",
          help="embedding dimension of the decoder target tokens",
      )
      parser.add_argument(
          "--transformer-dec-config",
          type=str,
          metavar="EXPR",
          help="""
      a tuple containing the configuration of the decoder transformer layers
      configurations:
      [(input_dim,
        num_heads,
        ffn_dim,
        normalize_before,
        dropout,
        attention_dropout,
        relu_dropout), ...]
              """,
      )
      parser.add_argument(
          "--conv-dec-config",
          type=str,
          metavar="EXPR",
          help="""
      an array of tuples for the decoder 1-D convolution config
          [(out_channels, conv_kernel_size, use_layer_norm), ...]""",
      )
  
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 VGGTransformerModel obj's underlying member method code with example usage: obj.add_args(parser) without return types