How can i sum up multiple inputs in one when using a submodel?

Question

I wrote a custom Tree-RNN-CELL that can handle several different inputs when they are provided as a tuple.

...
treeCell3_1 = TreeRNNCell(units=encodingBitLength, num_children=2)
RNNC = layers.RNN(treeCell3_1, return_state=True, return_sequences=True)
h_c_batch, h, c = RNNC(
    inputs=(h_batch2_1, c_batch2_1, h_batch2_2, c_batch2_2))

This is working fine, but now I wanted to put it together in a submodel, so that i can sum the 4 lines up in 2 lines and to have a better overview ( the tree gets big so its worth it)

class TreeCellModel(tf.keras.Model):

    def __init__(self, units, num_children):
        super().__init__()
        self.units = units
        self.num_children = num_children
        self.treeCell = TreeRNNCell(units=units, num_children=num_children)
        self.treeRNN = layers.RNN(self.treeCell, return_state=True, return_sequences=True)

    def call(self, inputs, **kwargs):

        h_c_batch, h, c = self.treeRNN(inputs=(inputs))
        h_batch, c_batch = AddCellStatesLayer(units=self.units)(h_c_batch)

        return h_batch, c_batch

treeCell2_1 = TreeCellModel(units=encodingBitLength, num_children=2)
h_batch2_1, c_batch2_1 = treeCell1_1(inputs=(h_batch1_1, c_batch1_1, h_batch1_2, c_batch1_2))

But now i get this error: ValueError: Layer rnn expects 1 input(s), but it received 4 input tensors. Inputs received: [<tf.Tensor 'h_batch1_1' shape=(1, 5, 19) dtype=float32>, <tf.Tensor 'c_batch1_1' shape=(1, 5, 19) dtype=float32>, <tf.Tensor 'h_batch1_2' shape=(1, 5, 19) dtype=float32>, <tf.Tensor 'c_batch1_2' shape=(1, 5, 19) dtype=float32>]

I checked the error already, and normally it gets fixed when using a tuple around the inputs. But thats what I'm already doing. I also doublechecked by outputting the type of "inputs" and it is a tuple.

Help please.