// dataUsageCacheV2 contains a cache of data usage entries version 2.
type dataUsageCacheV2 struct {
	Info  dataUsageCacheInfo
	Cache map[string]dataUsageEntryV2
}

// dataUsageCacheV3 contains a cache of data usage entries version 3.
type dataUsageCacheV3 struct {
	Info  dataUsageCacheInfo
	Cache map[string]dataUsageEntryV3
}

// dataUsageCacheV4 contains a cache of data usage entries version 4.

	dataUsageObjName       = ".usage.json"
	dataUsageObjNamePath   = bucketMetaPrefix + SlashSeparator + dataUsageObjName
	dataUsageBloomName     = ".bloomcycle.bin"
	dataUsageBloomNamePath = bucketMetaPrefix + SlashSeparator + dataUsageBloomName

	backgroundHealInfoPath = bucketMetaPrefix + SlashSeparator + ".background-heal.json"

	dataUsageCacheName = ".usage-cache.bin"
)

	for _, usage := range dataUsageInfo.BucketsUsage {
		clusterBuckets++
		clusterSize += usage.Size
		clusterObjectsCount += usage.ObjectsCount
		clusterVersionsCount += usage.VersionsCount
		clusterDeleteMarkersCount += usage.DeleteMarkersCount
		for k, v := range usage.ObjectSizesHistogram {
			clusterObjectSizesHistogram[k] += v
		}
		for k, v := range usage.ObjectVersionsHistogram {

		Value:  1,
		Usage:  "bind N number of listeners per ADDRESS:PORT",
		EnvVar: "MINIO_LISTENERS",
		Hidden: true,
	},
	cli.StringFlag{
		Name:   "console-address",
		Usage:  "bind to a specific ADDRESS:PORT for embedded Console UI, ADDRESS can be an IP or hostname",
		EnvVar: "MINIO_CONSOLE_ADDRESS",
	},
	cli.DurationFlag{
		Name:   "shutdown-timeout",
		Value:  xhttp.DefaultShutdownTimeout,

}

func registerStringParameter(name, value, usage string) {
	rootCmd.Flags().String(name, value, usage)
	registerEnvironment(name, value, usage)
}

func registerIntegerParameter(name string, value int, usage string) {
	rootCmd.Flags().Int(name, value, usage)
	registerEnvironment(name, value, usage)
}

func registerBooleanParameter(name string, value bool, usage string) {
	rootCmd.Flags().Bool(name, value, usage)

	assert.Equal(t, m.Payload.(*zdsapi.WorkloadRequest_Add).Add.Uid, uid)
	// send ack so the server doesn't wait for us.
	sendAck(ztunClient)

	// second message should be the snap sent message
	m, fds = readRequest(t, ztunClient)
	assert.Equal(t, len(fds), 0)

	sent := m.Payload.(*zdsapi.WorkloadRequest_SnapshotSent).SnapshotSent
	if sent == nil {
		panic("expected snapshot sent")
	}

        varyHeaders = varyHeadersBuilder.build()

        val statusLine = StatusLine.parse(source.readUtf8LineStrict())
        protocol = statusLine.protocol
        code = statusLine.code
        message = statusLine.message
        val responseHeadersBuilder = Headers.Builder()
        val responseHeaderLineCount = readInt(source)
        for (i in 0 until responseHeaderLineCount) {

			deleteMarkers = madmin.DeleteMarkers{Count: dataUsageInfo.DeleteMarkersTotalCount}
			usage = madmin.Usage{Size: dataUsageInfo.ObjectsTotalSize}
		} else {
			buckets = madmin.Buckets{Error: err.Error()}
			objects = madmin.Objects{Error: err.Error()}
			deleteMarkers = madmin.DeleteMarkers{Error: err.Error()}
			usage = madmin.Usage{Error: err.Error()}
		}

		// Fetching the backend information

        (new), are missing on your machine, `import tensorflow` will print a
        warning message.
*   The `tensorflow` pip package is built with CUDA 10.1 and cuDNN 7.6.
*   `tf.keras`
    *   Experimental support for mixed precision is available on GPUs and Cloud
        TPUs. See
        [usage guide](https://www.tensorflow.org/guide/keras/mixed_precision).

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Operation* b = Placeholder(graph, s, "B");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Operation* two = ScalarConst(2, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Operation* plus2 = Add(a, two, graph, s, "plus2");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);