def to_wider_model(self, pre_layer_id, n_add):
        """Widen the last dimension of the output of the pre_layer.
        Args:
            pre_layer_id: The ID of a convolutional layer or dense layer.
            n_add: The number of dimensions to add.
        """
        self.operation_history.append(("to_wider_model", pre_layer_id, n_add))
        pre_layer = self.layer_list[pre_layer_id]
        output_id = self.layer_id_to_output_node_ids[pre_layer_id][0]
        dim = layer_width(pre_layer)
        self.vis = {}
        self._search(output_id, dim, dim, n_add)
        # Update the tensor shapes.
        for u in self.topological_order:
            for v, layer_id in self.adj_list[u]:
                self.node_list[v].shape = self.layer_list[layer_id].output_shape
def to_wider_model ( self , pre_layer_id , n_add ) : self . operation_history . append ( ( "to_wider_model" , pre_layer_id , n_add ) ) pre_layer = self . layer_list [ pre_layer_id ] output_id = self . layer_id_to_output_node_ids [ pre_layer_id ] [ 0 ] dim = layer_width ( pre_layer ) self . vis = { } self . _search ( output_id , dim , dim , n_add ) for u in self . topological_order : for v , layer_id in self . adj_list [ u ] : self . node_list [ v ] . shape = self . layer_list [ layer_id ] . output_shape
Widen the last dimension of the output of the pre_layer.
        Args:
            pre_layer_id: The ID of a convolutional layer or dense layer.
            n_add: The number of dimensions to add.