code: def vggtransformer_2(args):
      args.input_feat_per_channel = getattr(
          args, "input_feat_per_channel", 80
      )
      args.vggblock_enc_config = getattr(
          args,
          "vggblock_enc_config",
          "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]",
      )
      args.transformer_enc_config = getattr(
          args,
          "transformer_enc_config",
          "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16",
      )
      args.enc_output_dim = getattr(args, "enc_output_dim", 1024)
      args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512)
      args.conv_dec_config = getattr(
          args, "conv_dec_config", "((256, 3, True),) * 4"
      )
      args.transformer_dec_config = getattr(
          args,
          "transformer_dec_config",
          "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 6",
      )
  
imports: import argparse
  import math
  from collections.abc import Iterable
  import torch
  import torch.nn
  from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LinearizedConvolution, TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock
label_desc: Use image node_id 9 for calling a global function with example usage: vggtransformer_2(args) without return types