  
  _LOSS_HELP = dict(
      gmsd=(
          "Gradient Magnitude Similarity Deviation seeks to match the global standard deviation of "
          "the pixel to pixel differences between two images. Similar in approach to SSIM. NB: This "
          "loss does not currently work on AMD cards."),
      l_inf_norm=(
          "The L_inf norm will reduce the largest individual pixel error in an image. As "
          "each largest error is minimized sequentially, the overall error is improved. This loss "
          "will be extremely focused on outliers."),
      laploss=(
 
</s> remove         if isinstance(loss_func, losses.GMSDLoss):
            pytest.skip("GMSD Loss is not currently compatible with PlaidML")
 </s> add         if isinstance(loss_func, losses.FocalFrequencyLoss):
            pytest.skip("FocalFrequencyLoss Loss is not currently compatible with PlaidML") </s> remove     if get_backend() == "amd" and isinstance(loss_func, losses.GMSDLoss):
        pytest.skip("GMSD Loss is not currently compatible with PlaidML")
 </s> add 
        "the pixel to pixel differences between two images. Similar in approach to SSIM. Ref: "
        "Gradient Magnitude Similarity Deviation: An Highly Efficient Perceptual Image Quality "
        "Index https://arxiv.org/ftp/arxiv/papers/1308/1308.3052.pdf"),
  
  _LOSS_HELP = dict(
      gmsd=(
          "Gradient Magnitude Similarity Deviation seeks to match the global standard deviation of "
        "the pixel to pixel differences between two images. Similar in approach to SSIM. Ref: "
        "Gradient Magnitude Similarity Deviation: An Highly Efficient Perceptual Image Quality "
        "Index https://arxiv.org/ftp/arxiv/papers/1308/1308.3052.pdf"),
        "the pixel to pixel differences between two images. Similar in approach to SSIM. Ref: "
        "Gradient Magnitude Similarity Deviation: An Highly Efficient Perceptual Image Quality "
        "Index https://arxiv.org/ftp/arxiv/papers/1308/1308.3052.pdf"),
      l_inf_norm=(
          "The L_inf norm will reduce the largest individual pixel error in an image. As "
          "each largest error is minimized sequentially, the overall error is improved. This loss "
          "will be extremely focused on outliers."),
      laploss=(
keep keep keep keep replace replace keep keep keep keep keep
 Loss updates
  - Split Loss to own package
  - Add Focal Frequency Loss (Nvidia only)
  - Enable GMSD for AMD 
plugins/train/_config.py