class_docstr: BERT language model pretraining.
class_docstr_tok: ["BERT","language","model","pretraining","."]
code: def _compute_losses(
      self, mlm_log_probs, nsp_logits, output_ids, output_mask, labels
  ):
      mlm_loss = self.mlm_loss(
          log_probs=mlm_log_probs,
          labels=output_ids,
          output_mask=output_mask,
      )
      if self.only_mlm_loss:
          loss, nsp_loss = mlm_loss, None
      else:
          nsp_loss = self.nsp_loss(logits=nsp_logits, labels=labels)
          loss = self.agg_loss(loss_1=mlm_loss, loss_2=nsp_loss)
      return mlm_loss, nsp_loss, loss
  
imports: import os
  from typing import Dict, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, CrossEntropyLoss, SmoothedCrossEntropyLoss
  from nemo.collections.common.metrics import Perplexity
  from nemo.collections.nlp.data.language_modeling.lm_bert_dataset import BertPretrainingDataset, BertPretrainingPreprocessedDataloader
  from nemo.collections.nlp.modules.common import BertPretrainingTokenClassifier, SequenceClassifier
  from nemo.collections.nlp.modules.common.lm_utils import get_lm_model
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.classes.modelPT import ModelPT
  from nemo.core.neural_types import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the BERTLMModel obj's underlying member method code with example usage: obj._compute_losses(mlm_log_probs, nsp_logits, output_ids, output_mask, labels) and returns: mlm_loss,  nsp_loss,  loss