code: def forward(self, img1, img2):
      (_, channel, _, _) = img1.size()
  
      if (
          channel == self.channel
          and self.window.data.type() == img1.data.type()
      ):
          window = self.window
      else:
          window = create_window(self.window_size, channel)
  
          if img1.is_cuda:
              window = window.cuda(img1.get_device())
          window = window.type_as(img1)
  
          self.window = window
          self.channel = channel
  
      return _ssim(
          img1,
          img2,
          window,
          self.window_size,
          channel,
          self.size_average,
      )
  
file_path: TTS/TTS/tts/utils/ssim.py
imports: from math import exp
  import torch
  import torch.nn.functional
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling the SSIM obj's underlying member method code with example usage: obj.forward(img1, img2) and returns: _ssim