"true" or "1", this environment variable makes `tf.nn.bias_add` operate
    deterministically (i.e. reproducibly), but currently only when XLA JIT
    compilation is *not* enabled. Setting `TF_DETERMINISTIC_OPS` to "true" or
    "1" also makes cuDNN convolution and max-pooling operate deterministically.
    This makes Keras Conv\*D and MaxPool\*D layers operate deterministically in

- github.com/gorilla/mux: [v1.7.0 → v1.7.3](https://github.com/gorilla/mux/compare/v1.7.0...v1.7.3)
- github.com/json-iterator/go: [v1.1.8 → v1.1.10](https://github.com/json-iterator/go/compare/v1.1.8...v1.1.10)
- github.com/jstemmer/go-junit-report: [af01ea7 → v0.9.1](https://github.com/jstemmer/go-junit-report/compare/af01ea7...v0.9.1)

1 is computed during the next loop. This is // possible because each iteration only uses T[i] in Step 2 and then // discards it in Step 6. d := bLimbs[i] c1 := addMulVVW(T[i:n+i], aLimbs, d) // Step 6 is replaced by shifting the virtual window we operate // over: T of the algorithm is T[i:] for us. That means that T1 in // Step 2 (T mod 2^_W) is simply T[i]. k0 in Step 3 is our m0inv. Y := T[i] * m.m0inv // Step 4 and 5 add Y × m to T, which as mentioned above is stored // at T[i:]. The two carries...