How do I train multiple timeseries at the same time?

I have several different time-series of different shapes(same number of samples), how would I train a model using several of these?

For example, say

(29276, 32, 4)
(29276, 5, 14)

The first layer in the model would look like:

self.CNN = tf.keras.Sequential([
            layers.Conv1D(filters=32, kernel_size=3, activation="relu", input_shape=(features, steps), padding='same'),
            layers.MaxPool1D(pool_size=3, padding='same'),
# ... etc

However, the input_shape would only work on one input. Am I supposed to use a 2D Convolution for this task?