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 __init__(self, bce_weight: float):
      assert 0 <= bce_weight <= 1
      super().__init__()
      self._bce_module = torch.nn.BCEWithLogitsLoss()
      self._bce_weight = bce_weight
  
imports: import torch
label_desc: Use image node_id 1 to create a new CorefLoss object from inherited base classes: torch.nn with example: obj = CorefLoss(bce_weight)