code: def forward(self, output: List[torch.Tensor], *args, **kwargs):
      student_out = output[-1] / self.loss_config["student_temp"]
      student_out = student_out.chunk(self.loss_config["num_crops"])
  
      # teacher centering and sharpening
      teacher_out = F.softmax(
          (self.teacher_output - self.center) / self.teacher_temp,
          dim=-1,
      )
      teacher_out = teacher_out.detach().chunk(
          len(self.loss_config["crops_for_teacher"])
      )
  
      total_loss = 0
      n_loss_terms = 0
      for iq, q in enumerate(teacher_out):
          for v in range(len(student_out)):
              if v == iq:
                  # we skip cases where student and teacher operate on the same view
                  continue
              loss = torch.sum(
                  -q * F.log_softmax(student_out[v], dim=-1), dim=-1
              )
              total_loss += loss.mean()
              n_loss_terms += 1
      total_loss /= n_loss_terms
  
      self.update_center()
      return total_loss
  
imports: import logging
  import pprint
  from typing import List
  import torch
  import torch.distributed
  import torch.nn.functional
  from classy_vision.generic.distributed_util import get_cuda_device_index, get_world_size, is_distributed_training_run
  from classy_vision.losses import ClassyLoss, register_loss
  from fairscale.nn import FullyShardedDataParallel
  from vissl.config import AttrDict
  from vissl.models.model_helpers import get_no_ddp_model
label_desc: Use image node_id 6 for calling the DINOLoss obj's underlying member method code with example usage: obj.forward(output) and returns: total_loss