code: def base_architecture_enconly(args):
      args.input_feat_per_channel = getattr(
          args, "input_feat_per_channel", 40
      )
      args.vggblock_enc_config = getattr(
          args, "vggblock_enc_config", "[(32, 3, 2, 2, True)] * 2"
      )
      args.transformer_enc_config = getattr(
          args,
          "transformer_enc_config",
          "((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2",
      )
      args.enc_output_dim = getattr(args, "enc_output_dim", 512)
      args.in_channels = getattr(args, "in_channels", 1)
      args.transformer_context = getattr(
          args, "transformer_context", "None"
      )
      args.transformer_sampling = getattr(
          args, "transformer_sampling", "None"
      )
  
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 11 for calling a global function with example usage: base_architecture_enconly(args) without return types