code: def view_loss_fn(
      teacher_logits,
      student_logits,
      teacher_temp,
      student_temp,
      centers,
      eps=1e-20,
  ):
      teacher_logits = teacher_logits.detach()
      student_probs = (student_logits / student_temp).softmax(dim=-1)
      teacher_probs = (
          (teacher_logits - centers) / teacher_temp
      ).softmax(dim=-1)
      return (
          -(teacher_probs * log(student_probs, eps)).sum(dim=-1).mean()
      )
  
imports: import copy
  import random
  from functools import wraps, partial
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from torchvision import transforms
  from einops import rearrange, reduce, repeat
label_desc: Use image node_id 7 for calling a global function with example usage: view_loss_fn(teacher_logits, student_logits, teacher_temp, student_temp, centers, eps) and returns: unknown