code: def forward(
      self,
      x,
      return_embedding=False,
      return_projection=True,
      student_temp=None,
      teacher_temp=None,
  ):
      if return_embedding:
          return self.student_encoder(
              x, return_projection=return_projection
          )
  
      image_one, image_two = self.augment1(x), self.augment2(x)
  
      local_image_one, local_image_two = self.local_crop(
          image_one
      ), self.local_crop(image_two)
      global_image_one, global_image_two = self.global_crop(
          image_one
      ), self.global_crop(image_two)
  
      (
          student_view_proj_one,
          student_region_proj_one,
          student_latent_one,
      ) = self.student_encoder(local_image_one)
      (
          student_view_proj_two,
          student_region_proj_two,
          student_latent_two,
      ) = self.student_encoder(local_image_two)
  
      with torch.no_grad():
          teacher_encoder = self._get_teacher_encoder()
          (
              teacher_view_proj_one,
              teacher_region_proj_one,
              teacher_latent_one,
          ) = teacher_encoder(global_image_one)
          (
              teacher_view_proj_two,
              teacher_region_proj_two,
              teacher_latent_two,
          ) = teacher_encoder(global_image_two)
  
      view_loss_fn_ = partial(
          view_loss_fn,
          student_temp=default(student_temp, self.student_temp),
          teacher_temp=default(teacher_temp, self.teacher_temp),
          centers=self.teacher_view_centers,
      )
  
      region_loss_fn_ = partial(
          region_loss_fn,
          student_temp=default(student_temp, self.student_temp),
          teacher_temp=default(teacher_temp, self.teacher_temp),
          centers=self.teacher_region_centers,
      )
  
      # calculate view-level loss
  
      teacher_view_logits_avg = torch.cat(
          (teacher_view_proj_one, teacher_view_proj_two)
      ).mean(dim=0)
      self.last_teacher_view_centers.copy_(teacher_view_logits_avg)
  
      teacher_region_logits_avg = torch.cat(
          (teacher_region_proj_one, teacher_region_proj_two)
      ).mean(dim=(0, 1))
      self.last_teacher_region_centers.copy_(teacher_region_logits_avg)
  
      view_loss = (
          view_loss_fn_(teacher_view_proj_one, student_view_proj_two)
          + view_loss_fn_(teacher_view_proj_two, student_view_proj_one)
      ) / 2
  
      # calculate region-level loss
  
      region_loss = (
          region_loss_fn_(
              teacher_region_proj_one,
              student_region_proj_two,
              teacher_latent_one,
              student_latent_two,
          )
          + region_loss_fn_(
              teacher_region_proj_two,
              student_region_proj_one,
              teacher_latent_two,
              student_latent_one,
          )
      ) / 2
  
      return (view_loss + region_loss) / 2
  
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 5 for calling the EsViTTrainer obj's underlying member method code with example usage: obj.forward(x, return_embedding, return_projection, student_temp, teacher_temp) and returns: unknown, self