class_docstr: Transformer-based Speech translation model from ESPNet-ST
  https://arxiv.org/abs/2004.10234
class_docstr_tok: ["Transformer-based","Speech","translation","model","from","ESPNet-ST","https",":","\/\/arxiv.org\/abs\/2004.10234"]
code: def get_normalized_probs(
      self,
      net_output: Tuple[
          Tensor, Optional[Dict[str, List[Optional[Tensor]]]]
      ],
      log_probs: bool,
      sample: Optional[Dict[str, Tensor]] = None,
  ):
      # net_output['encoder_out'] is a (B, T, D) tensor
      lprobs = self.get_normalized_probs_scriptable(
          net_output, log_probs, sample
      )
      if self.training:
          self.set_batch_first(lprobs)
      return lprobs
  
imports: import logging
  import math
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.speech_to_text.modules.convolution import infer_conv_output_dim
  from fairseq.models.transformer import Embedding, TransformerDecoder
  from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerEncoderLayer
label_desc: Use image node_id 7 for calling the ConvTransformerModel obj's underlying member method code with example usage: obj.get_normalized_probs(net_output, log_probs, sample) and returns: lprobs