code: def base_architecture(args):
      args.input_feat_per_channel = getattr(
          args, "input_feat_per_channel", 80
      )
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 2048
      )
      args.encoder_layers = getattr(args, "encoder_layers", 6)
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 8
      )
      args.encoder_normalize_before = getattr(
          args, "encoder_normalize_before", False
      )
      args.decoder_embed_dim = getattr(
          args, "decoder_embed_dim", args.encoder_embed_dim
      )
      args.decoder_ffn_embed_dim = getattr(
          args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
      )
      args.decoder_layers = getattr(args, "decoder_layers", 6)
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 8
      )
      args.decoder_normalize_before = getattr(
          args, "decoder_normalize_before", False
      )
      args.decoder_learned_pos = getattr(
          args, "decoder_learned_pos", False
      )
      args.attention_dropout = getattr(args, "attention_dropout", 0.0)
      args.activation_dropout = getattr(args, "activation_dropout", 0.0)
      args.activation_fn = getattr(args, "activation_fn", "relu")
      args.dropout = getattr(args, "dropout", 0.1)
      args.adaptive_softmax_cutoff = getattr(
          args, "adaptive_softmax_cutoff", None
      )
      args.adaptive_softmax_dropout = getattr(
          args, "adaptive_softmax_dropout", 0
      )
      args.share_decoder_input_output_embed = getattr(
          args, "share_decoder_input_output_embed", False
      )
      args.no_token_positional_embeddings = getattr(
          args, "no_token_positional_embeddings", False
      )
      args.adaptive_input = getattr(args, "adaptive_input", False)
      args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
  
      args.decoder_output_dim = getattr(
          args, "decoder_output_dim", args.decoder_embed_dim
      )
      args.decoder_input_dim = getattr(
          args, "decoder_input_dim", args.decoder_embed_dim
      )
      args.no_scale_embedding = getattr(
          args, "no_scale_embedding", False
      )
      args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
      args.max_source_positions = getattr(
          args, "max_source_positions", 3000
      )
      args.max_target_positions = getattr(
          args, "max_target_positions", 1024
      )
      args.tie_adaptive_weights = getattr(
          args, "tie_adaptive_weights", False
      )
      args.conv_out_channels = getattr(
          args, "conv_out_channels", args.encoder_embed_dim
      )
  
imports: import logging
  import math
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.speech_to_text.modules.convolution import infer_conv_output_dim
  from fairseq.models.transformer import Embedding, TransformerDecoder
  from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerEncoderLayer
label_desc: Use image node_id 1 for calling a global function with example usage: base_architecture(args) without return types