status reflects the aggregate status across all underlying cluster
jobs.

#### [alpha] Federated Horizontal Pod Autoscaling (HPA)

Federated HPAs are similar to the traditional Kubernetes HPAs, except
that they span multiple clusters. Creating a Federated HPA targeting
multiple clusters ensures that cluster-level autoscalers are
consistently deployed across those clusters, and dynamically managed

// Submitted by Tim Kramer <******@****.***>
rhcloud.com

// Redgate Software: https://red-gate.com
// Submitted by Andrew Farries <******@****.***>
instances.spawn.cc

// Render : https://render.com
// Submitted by Anurag Goel <******@****.***>
onrender.com
app.render.com

// Repl.it : https://repl.it
// Submitted by Lincoln Bergeson <******@****.***>

    *   Add support for TensorArrays to `tf.data Dataset`.
    *   Integrate Ragged Tensors with `tf.data`.
    *   All core and experimental tf.data transformations that input
        user-defined functions can span multiple devices now.
    *   Extending the TF 2.0 support for `shuffle(...,
        reshuffle_each_iteration=True)` and `cache()` to work across different
        Python iterators for the same dataset.

return // value is undefined. func ServiceIndicator() bool { return getIndicator() == indicatorTrue } // RecordApproved is an internal function that records the use of an approved // service. It does not override RecordNonApproved calls in the same span. // // It should be called by exposed functions that perform a whole cryptographic // alrgorithm (e.g. by Sum, not by New, unless a cryptographic Instantiate // algorithm is performed) and should be called after any checks that may cause // the function...