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. 
code: def _nlml(input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
      gold = torch.logsumexp(input_ + torch.log(target), dim=1)
      input_ = torch.logsumexp(input_, dim=1)
      return (input_ - gold).mean()
  
imports: import torch
label_desc: Use image node_id 4 for calling the CorefLoss obj's underlying member method code with example usage: obj._nlml(input_, target) and returns: unknown