backup-metadata: keep going when an ML data request fails (closes #101)

Each ML data request of up to 200 files is now tried on its own. A request
that still fails after its retries is logged, its files are written with the
reason in `mlDataError`, and the command exits 1 once the dump is complete.
`fetchMLData`, used only here, is removed in favour of a per-batch loop over
`fetchMLDataBatch`.

Model: opus-5-5

src/mldata-fetch.ts

@@ -5,9 +5,8 @@
 // comes back encrypted under the file's own key and gzipped; decrypting and
 // gunzipping yields the JSON payload
 // `{ face: { faces: [...] }, clip: { embedding } }`. Ente caps a request at 200
-// ids, so `fetchMLData` batches for callers that want many at once while
-// `fetchMLDataBatch` is the single-request unit the library submits to its
-// request pool.
+// ids, so callers that want many at once split them into batches of
+// `MLDATA_BATCH_SIZE` and call `fetchMLDataBatch` once per batch.
 
 import { gunzipSync } from "node:zlib";
 
@@ -69,25 +68,3 @@ export const fetchMLDataBatch = async (
     }
     return result;
 };
-
-// Fetch ML data for arbitrarily many ids, batching at `MLDATA_BATCH_SIZE`. Used
-// by the one-shot metadata backup; the library fetches through its request pool
-// with `fetchMLDataBatch` instead.
-export const fetchMLData = async (
-    api: ApiClient,
-    fileIDs: number[],
-    fileKeys: Map<number, Uint8Array>,
-): Promise<Map<number, MLData>> => {
-    const result = new Map<number, MLData>();
-    for (let i = 0; i < fileIDs.length; i += MLDATA_BATCH_SIZE) {
-        const batch = fileIDs.slice(i, i + MLDATA_BATCH_SIZE);
-        for (const [id, payload] of await fetchMLDataBatch(
-            api,
-            batch,
-            fileKeys,
-        )) {
-            result.set(id, payload);
-        }
-    }
-    return result;
-};