Request
        .Builder()
        .url(server.url("/"))
        .header("Range", "bytes=0-")
        .build()
    val call = client.newCall(request)

    // The response is not decompressed.
    val response = call.execute()
    assertThat(response.header("Content-Encoding")).isEqualTo("gzip")
    assertThat(response.body.source().readByteString()).isEqualTo(
      gzippedBody.snapshot(),
    )

     * application/zip=zip<br>
     * application/x-7z-comressed=7z<br>
     * application/x-bzip=bz<br>
     * application/x-bzip2=bz2<br>
     * application/x-tar=tar<br>
     * application/x-rar-compressed=rar<br>
     * video/3gp=3gp<br>
     * video/3g2=3g2<br>
     * video/x-msvideo=avi<br>
     * video/x-flv=flv<br>
     * video/mpeg=mpeg<br>
     * video/mp4=mp4<br>
     * video/ogv=ogv<br>

* New kubeDNS image fixes an issue where SRV records were incorrectly being compressed. Added manifest file for multiple arch images. ([#68430](https://github.com/kubernetes/kubernetes/pull/68430), [@prameshj](https://github.com/prameshj))

* Kubernetes now supports transparent compression of API responses. Clients that send `Accept-Encoding: gzip` will now receive a GZIP compressed response body if the API call was larger than 128KB.  Go clients automatically request gzip-encoding by default and should see reduced transfer times for very large API requests.  Clients in other languages may need to make changes to benefit from...

- New flag `--disable-compression-for-client-ips` can be used to control client address ranges for which traffic shouldn't be compressed. ([#111507](https://github.com/kubernetes/kubernetes/pull/111507), [@mborsz](https://github.com/mborsz)) [SIG API Machinery]

        round robin reads, users can guarantee that consumers get similar-sized
        examples in the same step.
    *   tf.data service now supports optional compression. Previously data would
        always be compressed, but now you can disable compression by passing
        `compression=None` to `tf.data.experimental.service.distribute(...)`.
    *   `tf.data.Dataset.batch()` now supports `num_parallel_calls` and