using the MGF1 function // specified in PKCS #1 v2.1. func mgf1XOR(out []byte, hash fips140.Hash, seed []byte) { var counter [4]byte var digest []byte done := 0 for done < len(out) { hash.Reset() hash.Write(seed) hash.Write(counter[0:4]) digest = hash.Sum(digest[:0]) for i := 0; i < len(digest) && done < len(out); i++ { out[done] ^= digest[i] done++ } incCounter(&counter) } } func emsaPSSEncode(mHash []byte, emBits int, salt []byte, hash fips140.Hash) ([]byte, error) { // See RFC 8017, Section 9.1.1....

That means that, for example, to apply authentication to all the *path operations* in a router it would end up being done in a different file, instead of keeping related logic together.

        `ImageDataGenerator`. It takes you from a structured directory of images
        to a labeled dataset, in one function call. Note that it doesn't perform
        image data augmentation (which is meant to be done using preprocessing
        layers).
    *   **[`text_dataset_from_directory`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text_dataset_from_directory)**

Traditionally, the Kubernetes scheduler was based on the assumption that additional persistent storage is available everywhere in the cluster and has infinite capacity. Topology constraints addressed the first point, but up to now pod scheduling was still done without considering that the remaining storage capacity may not be enough to start a new pod. [Storage capacity tracking](https://github.com/kubernetes/enhancements/tree/master/keps/sig-storage/1472-storage-capacity-tracking), a new alpha feature,...