code: def get_extra_losses(self, net_output):
      extra_losses = []
      names = []
  
      if "features_pen" in net_output:
          extra_losses.append(net_output["features_pen"])
          names.append("features_pen")
  
      return extra_losses, names
  
imports: import logging
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from omegaconf import II
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.data.dictionary import Dictionary
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.models.wav2vec.wav2vec2 import EXTRACTOR_MODE_CHOICES, MASKING_DISTRIBUTION_CHOICES, LAYER_TYPE_CHOICES, ConvFeatureExtractionModel, TransformerEncoder
  from fairseq.modules import GradMultiply, LayerNorm
  from fairseq.tasks.hubert_pretraining import HubertPretrainingConfig, HubertPretrainingTask
label_desc: Use image node_id 13 for calling the HubertModel obj's underlying member method code with example usage: obj.get_extra_losses(net_output) and returns: extra_losses,  names