code: def conformer_base_architecture(args):
      args.attn_type = getattr(args, "attn_type", None)
      args.pos_enc_type = getattr(args, "pos_enc_type", "abs")
      args.input_feat_per_channel = getattr(
          args, "input_feat_per_channel", 80
      )
      args.input_channels = getattr(args, "input_channels", 1)
      args.max_source_positions = getattr(
          args, "max_source_positions", 6000
      )
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 2048
      )
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 4
      )
      args.dropout = getattr(args, "dropout", 0.1)
      args.encoder_layers = getattr(args, "encoder_layers", 16)
      args.depthwise_conv_kernel_size = getattr(
          args, "depthwise_conv_kernel_size", 31
      )
      transformer_base_architecture(args)
  
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 1 for calling a global function with example usage: conformer_base_architecture(args) without return types