1D Convolution on multiple channels of varying length

Every datapoint in my dataset consists of 3 time series. The data in the time series is discretized into equal time-bins but the 3 time series were measured for varying length. Time series 1 has 10 bins, series 2 5 bins and series 3 only 1 single bin. So an exmaple datapoint looks like this

1, 4, 1, 7, 3, 7, 3, 7, 3, 1
9, 6, 4, 7, 1
4

I would like to run two 1D convolutional layers across all of these timeseries and then combine them with a fully connected layer. Now how would I go about this since the data consists of three entries with varying input sizes? I do think that it makes sense to share the weights of the conv layer between series 1 and 2 as they are fundamentally similar.

Note that I simplified the data for this example and the real data is much longer than 10, 5, 1.