      predicted image and the difference is taken. The difference used is a smooth L1 norm
      (approximate to MAE but differable at zero) When used as a loss, its minimization will result
      in predicted images approaching the samelevel of sharpness / blurriness as the ground truth.
  
      TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty for Image Restoration,
      Chengwu Lu & Hua Huang, 2014 (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf)
  
      Parameters:
          y_true: The predicted frames at each scale.
          y_true: The ground truth frames at each scale
  
 
</s> remove     Calculates the first and second order gradient difference between pixels of an image in the
    x and y dimensions. These gradients are then compared between the ground truth and the
    predicted image and the difference is taken. The difference used is a smooth L1 norm
    (approximate to MAE but differable at zero) When used as a loss, its minimization will result
    in predicted images approaching the samelevel of sharpness / blurriness as the ground truth.
 </s> add     Calculates the first and second order gradient difference between pixels of
    an image in the x and y dimensions. These gradients are then compared between
    the ground truth and the predicted image and the difference is taken. When
    used as a loss, its minimization will result in predicted images approaching
    the same level of sharpness / blurriness as the ground truth. </s> remove         y_true: The predicted frames at each scale.
 </s> add         y_true: The predicted frames at each scale </s> remove         The GD loss.
 </s> add         The GD loss </s> remove         a: penalty factor. larger number give larger weight to large deviations
        c: scale factor used to adjust to the input scale (i.e. inputs of mean 1e-4 or 256)

 </s> add         alpha: penalty factor. larger number give larger weight to large deviations
        beta: scale factor used to adjust to the input scale (i.e. inputs of mean 1e-4 or 256 ) </s> remove             x: First set of images.
            y: Second set of images.
 </s> add             img1: First set of images.
            img2: Second set of images.
    TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty
    for Image Restoration, Chengwu Lu & Hua Huang, 2014
    (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf)
      predicted image and the difference is taken. The difference used is a smooth L1 norm
      (approximate to MAE but differable at zero) When used as a loss, its minimization will result
      in predicted images approaching the samelevel of sharpness / blurriness as the ground truth.
  
    TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty
    for Image Restoration, Chengwu Lu & Hua Huang, 2014
    (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf)
    TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty
    for Image Restoration, Chengwu Lu & Hua Huang, 2014
    (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf)
  
      Parameters:
          y_true: The predicted frames at each scale.
          y_true: The ground truth frames at each scale
  
keep keep keep keep replace replace keep keep keep keep keep
 documentation, pep8, style, clarity updates  - Prep for Segmentation (#812)

* documentation, pep8, style, clarity updates

* Update cli.py

* Update _config.py

remove extra mask and coverage

mask type as dropdown

* Update training_data.py

move coverage / LR to global
cut down on loss description

style change
losses working in PR

* simpler logging

* legacy update 
lib/model/losses.py