  from __future__ import absolute_import
  
  import logging
  
  import numpy as np
  import tensorflow as tf
  
  # Ignore linting errors from Tensorflow's thoroughly broken import system
 
</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         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     def call(self, y_true, y_pred):
 </s> add     def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor: </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
from typing import Tuple
  from __future__ import absolute_import
  
  import logging
from typing import Tuple
  
  import numpy as np
  import tensorflow as tf
  
  # Ignore linting errors from Tensorflow's thoroughly broken import system
keep keep add keep keep keep keep keep
 bugfix: distibuted training with dssim 
lib/model/losses_tf.py