code: def add_args(parser):
      S2TTransformerModel.add_args(parser)
      parser.add_argument(
          "--input-feat-per-channel",
          type=int,
          metavar="N",
          help="dimension of input features per channel",
      )
      parser.add_argument(
          "--input-channels",
          type=int,
          metavar="N",
          help="number of chennels of input features",
      )
      parser.add_argument(
          "--depthwise-conv-kernel-size",
          type=int,
          metavar="N",
          help="kernel size of depthwise convolution layers",
      )
      parser.add_argument(
          "--attn-type",
          type=str,
          metavar="STR",
          help="If not specified uses fairseq MHA. Other valid option is espnet",
      )
      parser.add_argument(
          "--pos-enc-type",
          type=str,
          metavar="STR",
          help="Must be specified in addition to attn-type=espnet for rel_pos and rope",
      )
  
imports: import logging
  import math
  from pathlib import Path
  import torch
  from fairseq import checkpoint_utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, register_model, register_model_architecture
  from fairseq.models.speech_to_text.modules.convolution import Conv1dSubsampler, Conv2dSubsampler
  from fairseq.models.speech_to_text.s2t_transformer import S2TTransformerEncoder, S2TTransformerModel
  from fairseq.models.speech_to_text.s2t_transformer import base_architecture
  from fairseq.modules import PositionalEmbedding, RelPositionalEncoding
  from fairseq.modules.conformer_layer import ConformerEncoderLayer
label_desc: Use image node_id 2 for calling the S2TConformerModel obj's underlying member method code with example usage: obj.add_args(parser) without return types