stomioka/sdtm_mapper ? reverse-engineered prompt

Build me a Python tool that helps with CDISC SDTM mapping using AI.

I want to give it a SAS dataset, either from a local folder or an S3 bucket, and have it automatically generate a machine readable mapping specification with the dataset attributes filled in, so I do not have to run Proc Contents in SAS. Then it should be able to use a trained model to suggest the mapping for SDTM variables, and also let me train my own model from my own study data so it gets better for my use case.

Please include the basic package code, a simple notebook based workflow, and a few example tutorials showing how to prepare training data and how to run the mapper on new data. If there are pre trained models, wire them in so the tool works out of the box. Keep it focused on clinical trial AE style mapping, and make it easy to extend later for other SDTM domains. Look up current docs online if you need to.