Binary Classification Comparing two time series of variable length

Is there a machine learning model (something like LSTM or 1D-CNN) that takes two time series of variable length as input and outputs a binary classification (True/False whether time series are of same label)?

So the data would look something like the following

date        value label
2020-01-01  2     0     # first input time series
2020-01-02  1     0     # first input time series
2020-01-03  1     0     # first input time series
2020-01-01  3     1     # second input time series
2020-01-03  1     1     # second input time series

Is there something like that available out of the box, and if not how would you build a minimal working example model in Keras?

My best guess is to use a shared LSTM layer for both inputs and Concatenate both resulting vectors before feeding to the final Dense layer.

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

n_lstm_blocks = 50

input_1 = keras.Input(shape=(None, 1)) # unknown timespan, fixed feature size 1
input_2 = keras.Input(shape=(None, 1))
shared_lstm = layers.LSTM(n_lstm_blocks)
encode_1 = shared_lstm(input_1)
encode_2 = shared_lstm(input_2)
concat = layers.concatenate([encode_1,encode_2])
output = layers.Dense(1, activation='sigmoid')(concat)
model = keras.Model(inputs=[input_1,input_2],outputs=output)
model.compile(optimizer='adam', loss='binary_crossentropy')

A comparable task would be Siamese Networks / One-Shot learning which is used for face recognition. But in this case the task is to compare to time series and detect if they are of the same label, but knowing each label is NOT task of the network!