          if self._mask_inputs is None:
              return None
          return [K.int_shape(mask_input) for mask_input in self._mask_inputs]
  
      def configure(self, inputs, outputs):
          """ Configure the loss functions for the given inputs and outputs.
  
          Parameters
          ----------
          inputs: list
 
</s> remove         inputs: list
            A list of input tensors to the model in the order ("a", "b")
        outputs: list
            A list of output tensors to the model in the order ("a", "b")
 </s> add         model: :class:`keras.models.Model`
            The model that is to be trained </s> remove         Returns
        -------
        list
            A list of loss functions to apply to the model
 </s> add         Adds the loss functions to the :attr:`functions` dictionary.

        Parameters
        ----------
        output_names: list
            The output names from the model </s> remove     @classmethod
    def _get_loss_names(cls, outputs):
        """ Name the losses based on model output
 </s> add     def _get_loss_names(self, outputs):
        """ Name the losses based on model output.

        This is used for correct naming in the state file, for display purposes only.

        Adds the loss names to :attr:`names` </s> remove         self._names = None
        self._funcs = None
 </s> add         self._names = []
        self._funcs = dict() </s> remove         """ list: The list of loss functions for the model. """
 </s> add         """ dict: The loss functions that apply to each model output. """
    def configure(self, model):
          if self._mask_inputs is None:
              return None
          return [K.int_shape(mask_input) for mask_input in self._mask_inputs]
  
    def configure(self, model):
          """ Configure the loss functions for the given inputs and outputs.
  
          Parameters
          ----------
          inputs: list
keep keep keep keep replace keep keep keep keep keep
 training - Use explicit loss dictionaries to map to outputs 
plugins/train/model/_base.py