You can reference this tutorial on predicting time series data: Time series forecasting  |  TensorFlow Core 8
It does not require to check for stationarity before training the model. However, in some cases the model will benefit if you generate rolling and cumulative features based on the original time series values.
If you asked about the functions similar to those that are available in statsmodels, I did not come across a ready solutions. TF is a huge library. Probably you can do something with tf.math.segment_mean function or other math functions.