class_docstr: Transformers with convolutional context for ASR
  https://arxiv.org/abs/1904.11660
class_docstr_tok: ["Transformers","with","convolutional","context","for","ASR","https",":","\/\/arxiv.org\/abs\/1904.11660"]
code: def build_encoder(cls, args, task):
      return VGGTransformerEncoder(
          input_feat_per_channel=args.input_feat_per_channel,
          vggblock_config=eval(args.vggblock_enc_config),
          transformer_config=eval(args.transformer_enc_config),
          encoder_output_dim=args.enc_output_dim,
          in_channels=args.in_channels,
      )
  
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 3 for calling the VGGTransformerModel obj's underlying member method code with example usage: obj.build_encoder(cls, args, task) and returns: VGGTransformerEncoder