          -------
          tuple
              (n_true, n_pred): The ground truth and predicted value tensors with the mask applied
          """
          try:
              if mask_channel == -1:
                  logger.debug("No mask to apply")
                  return y_true[..., :3], y_pred[..., :3]
  
              logger.debug("Applying mask from channel %s", mask_channel)
              mask = K.expand_dims(y_true[..., mask_channel], axis=-1)
              mask_as_k_inv_prop = 1 - mask_prop
              mask = (mask * mask_prop) + mask_as_k_inv_prop
  
              n_true = K.concatenate([y_true[:, :, :, i:i+1] * mask for i in range(3)], axis=-1)
              n_pred = K.concatenate([y_pred[:, :, :, i:i+1] * mask for i in range(3)], axis=-1)
  
          except:
              logger.error("You have hit a bug which is being actively tracked by the developer.")
              logger.error("Please provide the following information so it can be fixed.")
              logger.error("y_true: %s, %s, y_pred: %s, %s, mask_channel: %s, mask_prop: %s",
                           K.int_shape(y_true), K.dtype(y_true), K.int_shape(y_pred),
                           K.dtype(y_pred), mask_channel, mask_prop)
              raise
  
  
          return n_true, n_pred
 
</s> remove             batch = batch[..., [2, 1, 0, 3]]
 </s> add             batch[..., :3] = batch[..., [2, 1, 0]]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
          -------
          tuple
              (n_true, n_pred): The ground truth and predicted value tensors with the mask applied
          """
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
        if mask_channel == -1:
            logger.debug("No mask to apply")
            return y_true[..., :3], y_pred[..., :3]
  
  
          return n_true, n_pred
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep
 Bugfix - Training - Fix eye/mouth multiplier for some models 
lib/model/losses_tf.py