docstr: Cross-entropy between softmax outputs of the teacher and student networks.
docstr_tok: ["Cross-entropy","between","softmax","outputs","of","the","teacher","and","student","networks","."]
code: def forward(
      self, student_output_list, teacher_out_softmaxed_centered_list
  ):
      """
      Cross-entropy between softmax outputs of the teacher and student networks.
      """
      # TODO: Use cross_entropy_distribution here
      total_loss = 0
      for s in student_output_list:
          lsm = F.log_softmax(s / self.student_temp, dim=-1)
          for t in teacher_out_softmaxed_centered_list:
              loss = torch.sum(t * lsm, dim=-1)
              total_loss -= loss.mean()
      return total_loss
  
file_path: dinov2/dinov2/loss/dino_clstoken_loss.py
imports: import torch
  import torch.distributed
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 4 for calling the DINOLoss obj's underlying member method code with example usage: obj.forward(student_output_list, teacher_out_softmaxed_centered_list) and returns: total_loss