code: def _ssim(
      self, img1, img2, window, window_size, channel, size_average=True
  ):
      mu1 = F.conv2d(
          img1, window, padding=window_size // 2, groups=channel
      )
      mu2 = F.conv2d(
          img2, window, padding=window_size // 2, groups=channel
      )
      mu1_sq = mu1.pow(2)
      mu2_sq = mu2.pow(2)
      mu1_mu2 = mu1 * mu2
  
      sigma1_sq = (
          F.conv2d(
              img1 * img1,
              window,
              padding=window_size // 2,
              groups=channel,
          )
          - mu1_sq
      )
      sigma2_sq = (
          F.conv2d(
              img2 * img2,
              window,
              padding=window_size // 2,
              groups=channel,
          )
          - mu2_sq
      )
      sigma12 = (
          F.conv2d(
              img1 * img2,
              window,
              padding=window_size // 2,
              groups=channel,
          )
          - mu1_mu2
      )
  
      C1 = 0.01**2
      C2 = 0.03**2
      ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
          1e-12 + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
      )
  
      if size_average:
          return ssim_map.mean()
      else:
          return ssim_map.mean([1, 2, 3])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppdet.core.workspace import register
  from ppdet.modeling import ops
  from ppdet.modeling.losses.iou_loss import GIoULoss
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 4 for calling the SSIM obj's underlying member method code with example usage: obj._ssim(img1, img2, window, window_size, channel, size_average) and returns: ssim_map, ssim_map