`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,...

pkg archive/tar, var ErrInsecurePath error #55356
pkg archive/zip, var ErrInsecurePath error #55356
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pkg bytes, func CutPrefix([]uint8, []uint8) ([]uint8, bool) #42537
pkg bytes, func CutSuffix([]uint8, []uint8) ([]uint8, bool) #42537
pkg context, func Cause(Context) error #51365
pkg context, func WithCancelCause(Context) (Context, CancelCauseFunc) #51365
pkg context, type CancelCauseFunc func(error) #51365