code: def forward(
      self, img, labels, temperature=None, alpha=None, **kwargs
  ):
      b, *_ = img.shape
      alpha = alpha if exists(alpha) else self.alpha
      T = temperature if exists(temperature) else self.temperature
  
      with torch.no_grad():
          teacher_logits = self.teacher(img)
  
      student_logits, distill_tokens = self.student(
          img, distill_token=self.distillation_token, **kwargs
      )
      distill_logits = self.distill_mlp(distill_tokens)
  
      loss = F.cross_entropy(student_logits, labels)
  
      if not self.hard:
          distill_loss = F.kl_div(
              F.log_softmax(distill_logits / T, dim=-1),
              F.softmax(teacher_logits / T, dim=-1).detach(),
              reduction="batchmean",
          )
          distill_loss *= T**2
  
      else:
          teacher_labels = teacher_logits.argmax(dim=-1)
          distill_loss = F.cross_entropy(distill_logits, teacher_labels)
  
      return loss * (1 - alpha) + distill_loss * alpha
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from vit_pytorch.vit import ViT
  from vit_pytorch.t2t import T2TViT
  from vit_pytorch.efficient import ViT
  from einops import rearrange, repeat
label_desc: Use image node_id 2 for calling the DistillWrapper obj's underlying member method code with example usage: obj.forward(img, labels, temperature, alpha) and returns: unknown