code: def __init__(
      self, *, teacher, student, temperature=1.0, alpha=0.5, hard=False
  ):
      super().__init__()
      assert isinstance(
          student,
          (DistillableViT, DistillableT2TViT, DistillableEfficientViT),
      ), "student must be a vision transformer"
  
      self.teacher = teacher
      self.student = student
  
      dim = student.dim
      num_classes = student.num_classes
      self.temperature = temperature
      self.alpha = alpha
      self.hard = hard
  
      self.distillation_token = nn.Parameter(torch.randn(1, 1, dim))
  
      self.distill_mlp = nn.Sequential(
          nn.LayerNorm(dim), nn.Linear(dim, num_classes)
      )
  
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 1 to create a new DistillWrapper object from inherited base classes: nn with example: obj = DistillWrapper()