          """ Add the given loss function with the given weight to the loss function chain.
  
          Parameters
          ----------
          function: :class:`keras.losses.Loss`
              The loss function to add to the loss chain
          weight: float, optional
              The weighting to apply to the loss function. Default: `1.0`
          mask_channel: int, optional
              The channel in the `y_true` image that the mask exists in. Set to `-1` if there is no
 
</s> remove     def add_loss(self, function, weight=1.0, mask_channel=-1):
 </s> add     def add_loss(self,
                 function: tf.keras.losses.Loss,
                 weight: float = 1.0,
                 mask_channel: int = -1) -> None: </s> remove         If a mask is being applied to the loss, then the appropriate mask is extracted from y_true
        and added as the 4th channel being passed to the penalized loss function.
 </s> add         If masks are being applied to the loss function inputs, then they should be included as
        additional channels at the end of :param:`y_true`, so that they can be split off and
        applied to the actual inputs to the selected loss function(s). </s> remove         y_true: tensor or variable
            The ground truth value
        y_pred: tensor or variable
            The predicted value
 </s> add         y_true: :class:`tensorflow.Tensor`
            The ground truth batch of images, with any required masks stacked on the end
        y_pred: :class:`tensorflow.Tensor`
            The batch of model predictions </s> remove         Weights are returned as the weighted sum of the chosen losses.
 </s> add         Loss is returned as the weighted sum of the chosen losses. </s> remove         tuple
            (n_true, n_pred): The ground truth and predicted value tensors with the mask applied
 </s> add         tf.Tensor
            The ground truth batch of images, with the required mask applied
        tf.Tensor
            The predicted batch of images with the required mask applied
        function: :class:`tf.keras.losses.Loss`
          """ Add the given loss function with the given weight to the loss function chain.
  
          Parameters
          ----------
        function: :class:`tf.keras.losses.Loss`
              The loss function to add to the loss chain
          weight: float, optional
              The weighting to apply to the loss function. Default: `1.0`
          mask_channel: int, optional
              The channel in the `y_true` image that the mask exists in. Set to `-1` if there is no
keep keep keep keep replace keep keep keep keep keep
 bugfix: distibuted training with dssim 
lib/model/losses_tf.py