class_docstr: Module for calculating AT Loss
  
  :param norm_type (int): Norm to be used in calculating loss
docstr: Forward function
  
  :param teacher_output (torch.FloatTensor): Prediction made by the teacher model
  :param student_output (torch.FloatTensor): Prediction made by the student model
docstr_tok: ["Forward","function",":","param","teacher_output","(","torch.FloatTensor",")",":","Prediction","made","by","the","teacher","model",":","param","student_output","(","torch.FloatTensor",")",":","Prediction","made","by","the","student","model"]
code: def forward(self, teacher_output, student_output):
      """
      Forward function
  
      :param teacher_output (torch.FloatTensor): Prediction made by the teacher model
      :param student_output (torch.FloatTensor): Prediction made by the student model
      """
  
      A_t = teacher_output  # [1:]
      A_s = student_output  # [1:]
  
      loss = 0.0
      for layerT, layerS in zip(A_t, A_s):
          xT = self.single_at_loss(layerT)
          xS = self.single_at_loss(layerS)
          loss += (xS - xT).pow(self.p).mean()
  
      return loss
  
imports: import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the ATLoss obj's underlying member method code with example usage: obj.forward(teacher_output, student_output) and returns: loss