// considered as Delete marker true to avoid listing such objects by
		// regular ListObjects() calls. However for delete replication this
		// ends up being a problem because "upon" a successful delete this
		// ends up creating a new delete marker that is spurious and unnecessary.
		//
		// Regression introduced by #14555 was reintroduced in #15564
		if versionFound {

nindent 4 }} {{- end }} spec: jobName: {{ template "minio.fullname" . }} {{- if .Values.tls.enabled }} tlsConfig: ca: secret: name: {{ .Values.tls.certSecret }} key: {{ .Values.tls.publicCrt }} serverName: {{ template "minio.fullname" . }} {{- end }} prober: url: {{ template "minio.fullname" . }}.{{ .Release.Namespace }}:{{ .Values.service.port }} path: /minio/v2/metrics/cluster {{- if .Values.tls.enabled }} scheme: https {{- else }} scheme: http {{- end }} {{- if .Values.metrics.serviceMonitor.relabelConfigsCluste...

		// finally convert it back to the server type (again via xml).
		// This is needed as there is no add-rule function in the server
		// yet.

		// Though we do not check if the rule already exists, this is
		// not a problem as we are always using the same replication
		// rule ID - if the rule already exists, it is just replaced.
		replicationConfigS, _, err := globalBucketMetadataSys.GetReplicationConfig(ctx, bucket)
		if err != nil {

       * `Enumeration<?>`, probably because of one of the following two bugs (and maybe those two
       * bugs are themselves just symptoms of the same underlying problem):
       *
       * https://github.com/typetools/checker-framework/issues/3030
       *
       * https://github.com/typetools/checker-framework/issues/3236
       */

## Breaking Changes

*   `tf.keras`:
    *   In `tf.keras.applications` the name of the "top" layer has been
        standardized to "predictions". This is only a problem if your code
        relies on the exact name of the layer.
    *   Huber loss function has been updated to be consistent with other Keras
        losses. It now computes mean over the last axis of per-sample losses

    * This can solve nuanced errors when using middlewares. Before Starlette `0.24.0`, a new instance of each middleware class would be created when a new middleware was added. That normally was not a problem, unless the middleware class expected to be created only once, with only one instance, that happened in some cases. This upgrade would solve those cases (thanks [@adriangb](https://github.com/adriangb)! Starlette PR [#2017](https://github.com/enc...