code: def get_activation(self, cls_score):
      fine_cls_score = cls_score[:, : self.num_classes]
  
      if not self.use_sigmoid:
          bg_score = cls_score[:, [-1]]
          new_score = torch.cat([fine_cls_score, bg_score], dim=-1)
          scores = F.softmax(new_score, dim=-1)
      else:
          score_classes = fine_cls_score.sigmoid()
          score_neg = 1 - score_classes.sum(dim=1, keepdim=True)
          score_neg = score_neg.clamp(min=0, max=1)
          scores = torch.cat([score_classes, score_neg], dim=1)
  
      return scores
  
imports: import warnings
  import torch
  import torch.nn
  import torch.nn.functional
  from mmdet.registry import MODELS
  from .accuracy import accuracy
  from .utils import weight_reduce_loss
label_desc: Use image node_id 3 for calling the CrossEntropyCustomLoss obj's underlying member method code with example usage: obj.get_activation(cls_score) and returns: scores