class_docstr: BCEWithLogitsLoss
  
  https://pytorch.org/docs/1.9.1/generated/torch.nn.BCEWithLogitsLoss.html
class_docstr_tok: ["BCEWithLogitsLoss","https",":","\/\/pytorch.org\/docs\/1.9.1\/generated\/torch.nn.BCEWithLogitsLoss.html"]
docstr: Args:
      logits: output of the classifier
      labels: ground truth labels
docstr_tok: ["Args",":","logits",":","output","of","the","classifier","labels",":","ground","truth","labels"]
code: def forward(
      self,
      logits: float,
      labels: List[int],
      loss_mask: torch.Tensor = None,
  ):
      """
      Args:
          logits: output of the classifier
          labels: ground truth labels
      """
      labels = torch.stack(labels)
      labels = labels.t().float()
  
      return super().forward(logits, labels)
  
file_path: NeMo/nemo/collections/common/losses/bce_logits_loss.py
imports: from typing import List
  import torch
  from torch import nn
  from nemo.core.classes import Serialization, Typing, typecheck
  from nemo.core.neural_types import LabelsType, LogitsType, LossType, MaskType, NeuralType
label_desc: Use image node_id 4 for calling the BCEWithLogitsLoss obj's underlying member method code with example usage: obj.forward(logits, labels, loss_mask) and returns: super