Test ML data with faces and no CLIP embedding, and name the norm

Each backup command's tests now have a file whose ML data holds faces and
no CLIP embedding; its JSON has the ML data and no keywords. The helper
that gives a vector's norm is called norm. The README names what a
video's JSON lacks, and the generator states its download size with the
Python packages counted.

Model: opus-5-5

README.md

@@ -1313,10 +1313,10 @@ photos newest first). `lib.subscribe({ onChange })` delivers a `LibraryChange`
   an ML data fetch, and rebuilds the on-disk backup tree with a durable failure
   ledger. Each file's JSON holds its ML data and keywords. An image's JSON also
   holds the EXIF and XMP of its original, and its dimensions if it is a JPEG. A
-  live photo's JSON holds these for its image. A video's JSON holds none of
-  them. A fetched original is written straight to its save path and not into the
-  cache, which then counts it as present; one the cache already held is copied
-  from there. `BackupOptions`: `downloadDirectory` (falls back to the
+  live photo's JSON holds these for its image. A video's JSON holds no EXIF, XMP
+  or dimensions. A fetched original is written straight to its save path and not
+  into the cache, which then counts it as present; one the cache already held is
+  copied from there. `BackupOptions`: `downloadDirectory` (falls back to the
   library's), `includeOriginals` (default `true`), `includeThumbnails` (default
   `false`), `onlyAlbumNames`, `verify` (default `false`), `onProgress`, and
   `lock`. See Backup layout above for the tree it writes, and for the albums