code: def base_architecture(args):
      args.dropout = getattr(args, "dropout", 0.1)
      args.output_frame_dim = getattr(args, "output_frame_dim", 80)
      args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64)
      # encoder prenet
      args.encoder_dropout = getattr(args, "encoder_dropout", 0.5)
      args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3)
      args.encoder_conv_kernel_size = getattr(
          args, "encoder_conv_kernel_size", 5
      )
      # encoder transformer layers
      args.encoder_transformer_layers = getattr(
          args, "encoder_transformer_layers", 6
      )
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim
      )
      args.encoder_normalize_before = getattr(
          args, "encoder_normalize_before", False
      )
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 4
      )
      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")
      # decoder prenet
      args.prenet_dropout = getattr(args, "prenet_dropout", 0.5)
      args.prenet_layers = getattr(args, "prenet_layers", 2)
      args.prenet_dim = getattr(args, "prenet_dim", 256)
      # decoder postnet
      args.postnet_dropout = getattr(args, "postnet_dropout", 0.5)
      args.postnet_layers = getattr(args, "postnet_layers", 5)
      args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512)
      args.postnet_conv_kernel_size = getattr(
          args, "postnet_conv_kernel_size", 5
      )
      # decoder transformer layers
      args.decoder_transformer_layers = getattr(
          args, "decoder_transformer_layers", 6
      )
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
      args.decoder_ffn_embed_dim = getattr(
          args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim
      )
      args.decoder_normalize_before = getattr(
          args, "decoder_normalize_before", False
      )
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 4
      )
  
imports: import logging
  from typing import List, Optional
  import torch
  from torch import nn
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
  from fairseq.models.text_to_speech.tacotron2 import Postnet, Prenet
  from fairseq.modules import FairseqDropout, LayerNorm, PositionalEmbedding, TransformerDecoderLayer, TransformerEncoderLayer
label_desc: Use image node_id 4 for calling a global function with example usage: base_architecture(args) without return types