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: Returns a weighted sum of two losses as a torch.Tensor 
docstr_tok: ["Returns","a","weighted","sum","of","two","losses","as","a","torch.Tensor"]
code: def forward(
      self,  # type: ignore  # pylint: disable=arguments-differ  #35566 in pytorch
      input_: torch.Tensor,
      target: torch.Tensor,
  ) -> torch.Tensor:
      """Returns a weighted sum of two losses as a torch.Tensor"""
      return (
          self._nlml(input_, target)
          + self._bce(input_, target) * self._bce_weight
      )
  
imports: import torch
label_desc: Use image node_id 2 for calling the CorefLoss obj's underlying member method code with example usage: obj.forward(input_, target) and returns: unknown