code: from torch.autograd import Variable
  import torch.nn.functional as F
  from torch import nn
  from torch import Tensor
  from torch.nn import Parameter
  
  def l2normalize(v, eps=1e-12):
      return v / (v.norm() + eps)
  
  
  class SpectralNorm(nn.Module):
      def __init__(self, module, name='weight', power_iterations=1):
          super(SpectralNorm, self).__init__()
          self.module = module
          self.name = name
          self.power_iterations = power_iterations
          if not self._made_params():
              self._make_params()
  
      def _update_u_v(self):
          u = getattr(self.module, self.name + "_u")
          v = getattr(self.module, self.name + "_v")
          w = getattr(self.module, self.name + "_bar")
  
          height = w.data.shape[0]
          for _ in range(self.power_iterations):
              v.data = l2normalize(torch.mv(torch.t(w.view(height,-1).data), u.data))
              u.data = l2normalize(torch.mv(w.view(height,-1).data, v.data))
  
          # sigma = torch.dot(u.data, torch.mv(w.view(height,-1).data, v.data))
          sigma = u.dot(w.view(height, -1).mv(v))
          setattr(self.module, self.name, w / sigma.expand_as(w))
  
      def _made_params(self):
          try:
              u = getattr(self.module, self.name + "_u")
              v = getattr(self.module, self.name + "_v")
              w = getattr(self.module, self.name + "_bar")
              return True
          except AttributeError:
              return False
  
  
      def _make_params(self):
          w = getattr(self.module, self.name)
  
          height = w.data.shape[0]
          width = w.view(height, -1).data.shape[1]
  
          u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
          v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
          u.data = l2normalize(u.data)
          v.data = l2normalize(v.data)
          w_bar = Parameter(w.data)
  
          del self.module._parameters[self.name]
  
          self.module.register_parameter(self.name + "_u", u)
          self.module.register_parameter(self.name + "_v", v)
          self.module.register_parameter(self.name + "_bar", w_bar)
  
  
      def forward(self, *args):
          self._update_u_v()
          return self.module.forward(*args)
apis: torch.nn.Parameter