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 get_normalized_probs(self, net_output, log_probs, sample=None):
      # net_output['encoder_out'] is a (B, T, D) tensor
      lprobs = super().get_normalized_probs(
          net_output, log_probs, sample
      )
      lprobs.batch_first = True
      return lprobs
  
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 6 for calling the VGGTransformerModel obj's underlying member method code with example usage: obj.get_normalized_probs(net_output, log_probs, sample) and returns: lprobs