class_docstr: Layer to compute the SSIM loss between a pair of images
      
class_docstr_tok: ["Layer","to","compute","the","SSIM","loss","between","a","pair","of","images"]
code: def predict(self, x, y):
      x = self.refl(x)
      y = self.refl(y)
  
      mu_x = self.mu_x_pool(x)
      mu_y = self.mu_y_pool(y)
  
      sigma_x = self.sig_x_pool(x**2) - mu_x**2
      sigma_y = self.sig_y_pool(y**2) - mu_y**2
      sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y
  
      SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2)
      SSIM_d = (mu_x**2 + mu_y**2 + self.C1) * (
          sigma_x + sigma_y + self.C2
      )
  
      return mx.nd.clip((1 - SSIM_n / SSIM_d) / 2, 0, 1)
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import mxnet
  from mxnet.gluon import nn
label_desc: Use image node_id 3 for calling the SSIM obj's underlying member method code with example usage: obj.predict(x, y) and returns: mx