class_docstr: STN which learns a k-dimensional linear transformation
  
  Arguments:
      nn (torch.nn.Module) -- module which takes feat_x as input and regresses it to a global feature used to calculate the transform
      nn_feat_size -- the size of the global feature
      k -- the size of trans_x
      batch_size -- the number of examples per batch
code: def get_orthogonal_regularization_loss(self):
      loss = torch.mean(
          torch.norm(
              torch.bmm(self.trans, self.trans.transpose(2, 1))
              - self.identity.to(self.trans.device).view(
                  -1, self.k, self.k
              ),
              dim=(1, 2),
          )
      )
  
      return loss
  
imports: import torch
  from torch.nn import Linear
label_desc: Use image node_id 3 for calling the BaseLinearTransformSTNkD obj's underlying member method code with example usage: obj.get_orthogonal_regularization_loss() and returns: loss