class_docstr: Hubert Pretrain model
class_docstr_tok: ["Hubert","Pretrain","model"]
code: def _calc_hubert_loss(
      self,
      encoder_out: Dict[str, torch.Tensor],
  ):
      # 1. Compute hubert loss
      loss, logp_m_list, logp_u_list = self.criterion_hubert(
          self.encoder.encoder, encoder_out
      )
  
      corr_masked, count_masked = 0, 0
      corr_unmask, count_unmask = 0, 0
      with torch.no_grad():
          for i, logp_m in enumerate(logp_m_list):
              corr_m, count_m = self.compute_correct(logp_m)
              corr_masked += corr_m
              count_masked += count_m
          for i, logp_u in enumerate(logp_u_list):
              corr_u, count_u = self.compute_correct(logp_u)
              corr_unmask += corr_u
              count_unmask += count_u
  
      acc_m = corr_masked / (count_masked + 1e-10)
      acc_u = corr_unmask / (count_unmask + 1e-10)
  
      return loss, acc_m, acc_u
  
imports: import logging
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.hubert.hubert_loss import HubertPretrainLoss
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.e2e_asr_common import ErrorCalculator
label_desc: Use image node_id 7 for calling the HubertPretrainModel obj's underlying member method code with example usage: obj._calc_hubert_loss(encoder_out) and returns: loss,  acc_m,  acc_u