code: def get_extra_losses(self, net_output):
      pen = []
  
      if "prob_perplexity" in net_output:
          pen.append(
              (net_output["num_vars"] - net_output["prob_perplexity"])
              / net_output["num_vars"]
          )
  
      if "features_pen" in net_output:
          pen.append(net_output["features_pen"])
  
      return pen
  
imports: import math
  from dataclasses import dataclass, field
  from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.distributed.fully_sharded_data_parallel import FullyShardedDataParallel
  from fairseq.modules import Fp32GroupNorm, Fp32LayerNorm, GradMultiply, GumbelVectorQuantizer, LayerNorm, MultiheadAttention, RelPositionalEncoding, SamePad, TransposeLast
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
  from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import buffered_arange, index_put, is_xla_tensor
  from .utils import pad_to_multiple
label_desc: Use image node_id 13 for calling the Wav2Vec2Model obj's underlying member method code with example usage: obj.get_extra_losses(net_output) and returns: pen