Evaluation rows must be scaled using statistics learned from training data only. Finish the transformation so constant features and values outside the training range follow the specified behavior.
Build the implementation in Python under /app, with /app/run.sh as its entry point. Read one JSON request from standard input and write one JSON result to standard output. Input has nonempty rectangular finite numeric train rows and equally wide test rows. For each column use training min/max only; transform (x-min)/(max-min), or zero when the training column is constant. Test values may scale outside [0,1]. Return scaled test rows rounded to six decimals. Reject missing training rows, ragged rows, zero width or nonfinite values.
Reject inputs that violate an explicit validation rule with exit status 2, no standard output, and {"error":"invalid_input"} on standard error. For valid input, exit with status 0. Keep the implementation reusable for new inputs; it must work offline and must not depend on the example data being the only data it receives.
Repair or replace the unfinished /app/run.sh and add source files under /app. Malformed JSON must use the same invalid-input response. Do not read /tests or /oracle from the application.