class_docstr: See the rationale for using NLML in Lee et al. 2017
  https://www.aclweb.org/anthology/D17-1018/
  The added weighted summand of BCE helps the model learn even after
  converging on the NLML task. 
docstr: For numerical stability, clamps the input before passing it to BCE.
          
docstr_tok: ["For","numerical","stability",",","clamps","the","input","before","passing","it","to","BCE","."]
code: def _bce(
      self, input_: torch.Tensor, target: torch.Tensor
  ) -> torch.Tensor:
      """For numerical stability, clamps the input before passing it to BCE."""
      return self._bce_module(
          torch.clamp(input_, min=-50, max=50), target
      )
  
imports: import torch
label_desc: Use image node_id 3 for calling the CorefLoss obj's underlying member method code with example usage: obj._bce(input_, target) and returns: self