code: def forward(self, weight):
      inputs = {
          "Weight": weight,
          "U": self.weight_u,
          "V": self.weight_v,
      }
      out = self._helper.create_variable_for_type_inference(self._dtype)
      _power_iters = self._power_iters if self.training else 0
      self._helper.append_op(
          type="spectral_norm",
          inputs=inputs,
          outputs={
              "Out": out,
          },
          attrs={
              "dim": self._dim,
              "power_iters": _power_iters,
              "eps": self._eps,
          },
      )
  
      return out
  
imports: import base64
  import os
  import sys
  import cv2
  import numpy
  import paddle
  import paddle.nn
  from PIL import Image
label_desc: Use image node_id 2 for calling the _SpectralNorm obj's underlying member method code with example usage: obj.forward(weight) and returns: out