Ok, i tried base implementation as an SameDiffVertex without learnable parameter with help of:

a short test with SameDiff:

@Override
public SDVariable defineVertex(SameDiff sameDiff, VertexInputs inputs) {
SDVariable x1 = inputs.getInput(0);
// mean_i = sum(x_i[j] for j in range(k)) / k
SDVariable mean1 = x1.mean(0);
// var_i = sum((x_i[j] - mean_i) ** 2 for j in range(k)) / k
SDVariable var1 = x1.sub(mean1).pow(2).mean(0);
// x_i_normalized = (x_i - mean_i) / sqrt(var_i + epsilon)
SDVariable norm1 = x1.sub(mean1).div(sameDiff.math.square(var1.add(1e-10)));
return norm1;
}

Can anybody give me a short hint how this would map to an RNN Input Matrix. I know i get the base variable inside the defineVertex Method with something like that:

inputs.getInput(0)

or is this already enough if i use: x1 = inputs.getInput(0);