    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""",
        )
Add model-specific arguments to the parser.
examples/speech_recognition/models/vggtransformer.py