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 logging
  from collections import namedtuple
  import torch
  import torch.nn
  from fairseq import checkpoint_utils
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.models.speech_to_text import TransformerDecoder, S2TTransformerEncoder
  from fairseq.models.transformer import TransformerEncoder
  from fairseq.modules import TransformerEncoderLayer, GradMultiply, LayerNorm
label_desc: Use image node_id 7 for calling the DualInputS2TTransformerModel obj's underlying member method code with example usage: obj.get_normalized_probs(net_output, log_probs, sample) and returns: lprobs