Model interpretation modules

**Is your feature request related to a problem? Please describe.**
Now Stave can visualize NLP data results, such as annotation, link. Another type of valuable information to show to machine learning practitioners are the model insights (interpretability).

We can leverage toolkits for model interpretability, here is an example:
The paper: https://www.aclweb.org/anthology/2020.emnlp-demos.15.pdf
The code: https://github.com/pair-code/lit

According to this paper, the interpretable results are light-weight and stateless, so we can also pass them inside the DataPack.

**Describe the solution you'd like**
Here is a concrete plan of making this happen:
1. Create a sub-ontology that setup the data types needed for interpretability.
2. Run the interpreter models on the data packs (this workflow may start from a Stave click)
3. Store the interpretation results in the data pack using the sub-ontology.
4. Create a plugin that wraps around LIT for the visualization.

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context**
Add any other context or screenshots about the feature request here.

No files, tests, or entry points are identified. Start by mapping the existing DataPack ontology, interpreter workflow, and visualization plugin architecture; done would require the planned interpretability data types, model execution flow, stored results, and LIT-based visualization plugin.

Written by the indexing model from the issue text.