* asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, Function, Executor) Futures.transform}, but you will often
 * find it easier to use a framework. Frameworks automate the process, often adding features like
 * monitoring, debugging, and cancellation. Examples of frameworks include:
 *
 * <ul>

  * Retry Pod/RC updates in kubectl rolling-update.
  * Stop 'kubectl drain' deleting pods with local storage.
  * Add `kubectl rollout status`
* Security/Auth
  * L7 LB controller and disk attach controllers run on master, so nodes do not need those privileges.
  * Setting TLS1.2 minimum
  * `kubectl create secret tls` command
  * Webhook Token Authenticator

- When SchedulerQueueingHint is enabled,
  the scheduler's in-tree plugins now subscribe to specific node events to decide whether to requeue Pods.
  This allows the scheduler to handle cluster events faster with less memory.
  
  Specific node events include updates to taints, tolerations or allocatable.

- Kubeadm: removed the toleration for the `node-role.kubernetes.io/master` taint from the CoreDNS deployment of `kubeadm`. With the 1.25 release of kubeadm the taint `node-role.kubernetes.io/master` is no longer applied to control plane nodes and the toleration for it can be removed with the release of 1.26. You can also perform the same toleration removal from your own addon manifests....

    assertContent("mp3 data", response)
  }

  @Test
  fun ntripr1() {
    server.enqueue(
      MockResponse.Builder()
        .status("SOURCETABLE 200 OK")
        .addHeader("Server: NTRIP Caster 1.5.5/1.0")
        .addHeader("Date: 23/Jan/2004:08:54:59 UTC")
        .addHeader("Content-Type: text/plain")

     * changing any bin. The operations must not take any action that could even momentarily
     * cause a concurrent read operation to see inconsistent data. This is made easier by the
     * nature of the read operations in Map. For example, no operation can reveal that the table
     * has grown but the threshold has not yet been updated, so there are no atomicity requirements

Read more about sections `cluster_id`, `client_id` on [NATS documentation](https://github.com/nats-io/nats-streaming-server/blob/master/README.md). Section `maxPubAcksInflight` is explained [here](https://github.com/nats-io/stan.go#publisher-rate-limiting).

### Step 2: Enable NATS bucket notification using MinIO client

     * changing any bin. The operations must not take any action that could even momentarily
     * cause a concurrent read operation to see inconsistent data. This is made easier by the
     * nature of the read operations in Map. For example, no operation can reveal that the table
     * has grown but the threshold has not yet been updated, so there are no atomicity requirements

		// object SparkJSONRead {
		//   def main(args: Array[String]): Unit = {
		//     val spark:SparkSession = SparkSession.builder()
		//       .appName("SparkByExample")
		//       .master("local[1]").getOrCreate()
		//
		//     spark.sparkContext.setLogLevel("ERROR")
		//     spark.sparkContext.hadoopConfiguration.set("fs.s3a.endpoint", "http://minio-lb:9000")

 private:
  TF_Graph* graph_;
  int counter_;
};

// Helper macros for the TF_OperationGetAttr* tests.
// TODO(ashankar): Use gmock matchers instead?
// (https://github.com/google/googletest/blob/master/googlemock/docs/CookBook.md#writing-new-parameterized-matchers-quickly)
// That will require setting up the tensorflow build with gmock.
#define EXPECT_TF_META(attr_name, expected_list_size, expected_type, \