4d convolution, i want to add conv4d to my model but the custom function doesn't work

Question

I have a u-net module, i want to change the conv3d with conv4d, when i looked on google i found some custom conv4d functions but I am having hard time executing them.

can someone show me the right way to call them and remove the errors

here are the two functions i tryed (but i get errors but just executing them)

**first one i tryed in from : **> How to create a Keras layer to do a 4D convolutions (Conv4D)?

class Conv(Layer):
    def __init__(self, filters, kernel_size, padding='VALID', **kwargs):
        self.filters = filters
        self.kernel_size = kernel_size #must be a tuple!!!!
        self.padding=padding

        super(Conv, self).__init__(**kwargs)

    #using channels last!!!
    def build(self, input_shape):
        spatialDims = len(self.kernel_size)
        allDims = len(input_shape)
        assert allDims == spatialDims + 2 #spatial dimensions + batch size + channels

        kernelShape = self.kernel_size + (input_shape[-1], self.filters)
            #(spatial1, spatial2,...., spatialN, input_channels, output_channels)

        biasShape = tuple(1 for _ in range(allDims-1)) + (self.filters,)


        self.kernel = self.add_weight(name='kernel', 
                                      shape=kernelShape,
                                      initializer='uniform',
                                      trainable=True)
        self.bias = self.add_weight(name='bias', 
                                    shape = biasShape, 
                                    initializer='zeros',
                                    trainable=True)
        self.built = True

    def call(self, inputs):
        results = tf.nn.convolution(inputs, self.kernel, padding=self.padding)
        return results + self.bias

    def compute_output_shape(self, input_shape):
        sizes = input_shape[1:-1]

        if (self.padding=='VALID') or (self.padding=='valid'):
            sizes = [s - kSize + 1 for s, kSize in zip(sizes, self.kernel_size)]

        return input_shape[:1] + sizes + (self.filters,)

**and second one check this: ** https://github.com/Vincentx15/Conv4D/blob/main/Conv4DTF.py