lock {\n  width: 100%;\n}\n\n.fade {\n  transition: opacity 0.15s linear;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .fade {\n    transition: none;\n  }\n}\n\n.fade:not(.show) {\n  opacity: 0;\n}\n\n.collapse:not(.show) {\n  display: none;\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  transition: height 0.35s ease;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .collapsing {\n    transition: none;\n  }\n}\n\n.collapsing.width {\n  width: 0;\n  height:...

lock {\n  width: 100%;\n}\n\n.fade {\n  transition: opacity 0.15s linear;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .fade {\n    transition: none;\n  }\n}\n\n.fade:not(.show) {\n  opacity: 0;\n}\n\n.collapse:not(.show) {\n  display: none;\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  transition: height 0.35s ease;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .collapsing {\n    transition: none;\n  }\n}\n\n.dropup,\n.dropright,\n.dropdown,\n.dropleft...

        and `tf.data.Dataset.with_options()` respectively.
    *   New `tf.data.Dataset.reduce()` API allows users to reduce a finite
        dataset to a single element using a user-provided reduce function.
    *   New `tf.data.Dataset.window()` API allows users to create finite windows
        of input dataset; when combined with the `tf.data.Dataset.reduce()` API,
        this allows users to implement customized batching.

### Immutable Secrets and ConfigMaps (beta)

Secret and ConfigMap volumes can be marked as immutable, which significantly reduces load on the API server if there are many Secret and ConfigMap volumes in the cluster.