How to properly setup jensen_shannon_divergence and infinity norm in tensorflow data validation for skew and drift checks

Tensorflow data validation offers the capability of checking data skew and drift and the documentation also mention that Setting the correct distance is typically an iterative process requiring domain knowledge and experimentation

How can one specify an initial reasonable jensen_shannon_divergence threshold (or infinity_norm one for categorical features)? Is there some python package/utility/code that one can leverage on a given data set feature to compute a reasonable threshold?

If not, what is recommended to proper conduct the experiment and find the most appropriate thhreshold for a given feature in a dataset?