code: def entropy_logits(linear_output):
      """Computes entropy logits in CDAN with entropy conditioning (CDAN+E)
  
      Examples:
          See CDANTrainer in kale.pipeline.domain_adapter
      """
      p = F.softmax(linear_output, dim=1)
      loss_ent = -torch.sum(p * (torch.log(p + 1e-5)), dim=1)
      return loss_ent
  
file_path: pykale/kale/predict/losses.py
imports: import torch
  import torch.nn
  from torch.autograd import grad
  from torch.nn import functional
label_desc: Use image node_id 4 for calling a global function with example usage: entropy_logits(linear_output) and returns: loss_ent