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"]
docstr: Build a new model instance.
docstr_tok: ["Build","a","new","model","instance","."]
code: def build_model(cls, args, task):
      """Build a new model instance."""
      # make sure that all args are properly defaulted
      # (in case there are any new ones)
      base_architecture(args)
  
      encoder = cls.build_encoder(args, task)
      decoder = cls.build_decoder(args, task)
      return cls(encoder, decoder)
  
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 5 for calling the VGGTransformerModel obj's underlying member method code with example usage: obj.build_model(cls, args, task) and returns: cls