code: def region_loss_fn(
      teacher_logits,
      student_logits,
      teacher_latent,
      student_latent,
      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)
  
      sim_matrix = einsum(
          "b i d, b j d -> b i j", student_latent, teacher_latent
      )
      sim_indices = sim_matrix.max(dim=-1).indices
      sim_indices = repeat(
          sim_indices, "b n -> b n k", k=teacher_probs.shape[-1]
      )
      max_sim_teacher_probs = teacher_probs.gather(1, sim_indices)
  
      return (
          -(max_sim_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 8 for calling a global function with example usage: region_loss_fn(teacher_logits, student_logits, teacher_latent, student_latent, teacher_temp, student_temp, centers, eps) and returns: unknown