  
          Parameters
          ----------
          batch: :class:`numpy.ndarray`
              This should be a 4-dimensional array of training images in the format (`batchsize`,
              `height`, `width`, `channels`). Targets should be requested after performing image
              transformations but prior to performing warps.
  
          Returns
          -------
 
</s> remove         Returns the targets as a list of 4-dimensional :class:`numpy.ndarray` s of shape
        (`batchsize`, `height`, `width`, `3`).
 </s> add         Parameters
        ----------
        target_batch: list
            List of 4 dimension :class:`numpy.ndarray` objects resized the model outputs.
            The 4th channel of the array contains the face mask, any additional channels after
            this are additional masks (e.g. eye mask and mouth mask) </s> remove         The target masks are returned as its own item and is the 4th channel of the final target
        output.
 </s> add         Returns
        -------
        dict:
            The targets and the masks separated into their own items. The targets are a list of
            3 channel, 4 dimensional :class:`numpy.ndarray` objects sized for each output from the
            model. The masks are a :class:`numpy.ndarray` of the final output size. Any additional
            masks(e.g. eye and mouth masks) will be collated together into a :class:`numpy.ndarray`
            of the final output size. The number of channels will be the number of additional
            masks available </s> remove class PenalizedLoss():  # pylint:disable=too-few-public-methods
    """ Penalized Loss function.

    Applies the given loss function just to the masked area of the image.

    Parameters
    ----------
    loss_func: function
        The actual loss function to use
    mask_prop: float, optional
        The amount of mask propagation. Default: `1.0`
    """
    def __init__(self, loss_func, mask_prop=1.0):
        self._loss_func = loss_func
        self._mask_prop = mask_prop

    def __call__(self, y_true, y_pred):
        """ Apply the loss function to the masked area of the image.

        Parameters
        ----------
        y_true: tensor or variable
            The ground truth value. This should contain the mask in the 4th channel that will be
            split off for penalizing.
        y_pred: tensor or variable
            The predicted value

        Returns
        -------
        tensor
            The Loss value
        """
        mask = self._prepare_mask(K.expand_dims(y_true[..., -1], axis=-1))
        y_true = y_true[..., :-1]
        n_true = y_true * mask
        n_pred = y_pred * mask
        if isinstance(self._loss_func, DSSIMObjective):
            # Extract Image Patches in SSIM requires that y_pred be of a known shape, so
            # specifically reshape the tensor.
            n_pred = K.reshape(n_pred, K.int_shape(y_pred))
        return self._loss_func(n_true, n_pred)

    def _prepare_mask(self, mask):
        """ Prepare the masks for calculating loss

        Parameters
        ----------
        mask: :class:`numpy.ndarray`
            The masks for the current batch

        Returns
        -------
        tensor
            The prepared mask for applying to loss
        """
        mask_as_k_inv_prop = 1 - self._mask_prop
        mask = (mask * self._mask_prop) + mask_as_k_inv_prop
        return mask


 </s> add  </s> remove class PenalizedLoss(tf.keras.losses.Loss):
    """ Penalized Loss function.

    Applies the given loss function just to the masked area of the image.

    Parameters
    ----------
    loss_func: function
        The actual loss function to use
    mask_prop: float, optional
        The amount of mask propagation. Default: `1.0`
    """
    def __init__(self, loss_func, mask_prop=1.0):
        super().__init__(name="penalized_loss")
        self._loss_func = compile_utils.LossesContainer(loss_func)
        self._mask_prop = mask_prop

    def call(self, y_true, y_pred):
        """ Apply the loss function to the masked area of the image.

        Parameters
        ----------
        y_true: tensor or variable
            The ground truth value. This should contain the mask in the 4th channel that will be
            split off for penalizing.
        y_pred: tensor or variable
            The predicted value

        Returns
        -------
        tensor
            The Loss value
        """
        mask = self._prepare_mask(K.expand_dims(y_true[..., -1], axis=-1))
        y_true = y_true[..., :-1]
        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)
        return self._loss_func(n_true, n_pred)

    def _prepare_mask(self, mask):
        """ Prepare the masks for calculating loss

        Parameters
        ----------
        mask: :class:`numpy.ndarray`
            The masks for the current batch

        Returns
        -------
        tensor
            The prepared mask for applying to loss
        """
        mask_as_k_inv_prop = 1 - self._mask_prop
        mask = (mask * self._mask_prop) + mask_as_k_inv_prop
        return mask


 </s> add 
            This should be a 4+-dimensional array of training images in the format (`batchsize`,
  
          Parameters
          ----------
          batch: :class:`numpy.ndarray`
            This should be a 4+-dimensional array of training images in the format (`batchsize`,
              `height`, `width`, `channels`). Targets should be requested after performing image
              transformations but prior to performing warps.
  
          Returns
          -------
keep keep keep keep replace keep keep keep keep keep
 Add Mouth and Eye Priority to Loss options (#1054)

* Priority Training for Mouth and Eyes - Tensorflow

* Use chosen loss function for area multipliers

* loss multipliers for AMD

* Fix mask multipliers for plaid and roll PenalizedMaskLoss into LossWrapper

* losses_tf: roll PenalizedMaskLoss into LossWrapper 
lib/training_data.py