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,
  ):
      return self.get_normalized_probs_scriptable(
          net_output, log_probs, sample
      )
  
imports: import copy
  import logging
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  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, FairseqEncoderModel, FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder
  from fairseq.models.speech_to_text.hub_interface import S2THubInterface
  from fairseq.models.transformer import Embedding, TransformerDecoder, TransformerModelBase
  from fairseq.models.wav2vec import Wav2VecEncoder
  from fairseq.modules.layer_norm import LayerNorm
label_desc: Use image node_id 12 for calling the XMTransformerModel obj's underlying member method code with example usage: obj.get_normalized_probs(net_output, log_probs, sample) and returns: self