code: def get_normalized_probs(
      self,
      net_output,
      log_probs,
      sample=None,
  ):
      # net_output['encoder_out'] is a (B, T, D) tensor
      lprobs = self.get_normalized_probs_scriptable(
          net_output, log_probs, sample
      )
      lprobs.batch_first = True
      return lprobs
  
imports: import logging
  from collections import OrderedDict, namedtuple
  from typing import Dict, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.file_io import PathManager
  from fairseq.models import FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.speech_to_text import MultiInputDecoder, MultiModalityEncoder, SpeechWavTransformerEncoder, StackedSpeechWavTransformerEncoder
  from fairseq.models.transformer import TransformerDecoder, TransformerEncoder, TransformerModel
label_desc: Use image node_id 5 for calling the SpeechTextPreTrainModel obj's underlying member method code with example usage: obj.get_normalized_probs(net_output, log_probs, sample) and returns: lprobs