// atomic time of last success
	lastSuccess int64

	// atomic time of last time a token was grabbed.
	lastStarted int64

	// Atomic status of disk.
	status atomic.Int32

	// Atomic number indicates if a disk is hung
	waiting atomic.Int32
}

// newDiskHealthTracker creates a new disk health tracker.
func newDiskHealthTracker() *diskHealthTracker {
	d := diskHealthTracker{

			wantSelected: &inputSerialized[9],
			wantOk:       true,
		},
	}

	for testID, tt := range tests {
		rng := rand.New(rand.NewSource(0))
		// Run for a number of times, shuffling the input to ensure that output is consistent.
		for i := range 10 {
			t.Run(fmt.Sprintf("test-%d-%s-run-%d", testID, tt.name, i), func(t *testing.T) {
				if i > 0 {

## Replicação - Número de Processos { #replication-number-of-processes }

	checkRequestAuthType(ctx, r, policy.DeleteObjectAction, bucket, "")

	deleteObjectsFn := objectAPI.DeleteObjects

	// Return Malformed XML as S3 spec if the number of objects is empty
	if len(deleteObjectsReq.Objects) == 0 || len(deleteObjectsReq.Objects) > maxDeleteList {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrMalformedXML), r.URL)
		return
	}

    )
    pr = gh_repo.create_pull(title=message, body=body, base="master", head=branch_name)
    print(f"Created PR: {pr.number}")
    print("Finished")


if __name__ == "__main__":

`await` が機能するためには、非同期処理をサポートする関数内にある必要があります。これは、`async def` で関数を宣言するだけでよいです:

```Python hl_lines="1"
async def get_burgers(number: int):
    # ハンバーガーを作成するために非同期処理を実行
    return burgers
```

...`def` のかわりに:

```Python hl_lines="2"
# 非同期ではない
def get_sequential_burgers(number: int):
    # ハンバーガーを作成するためにシーケンシャルな処理を実行
    return burgers
```

  }

  void VerifyFDefNodes(const tensorflow::FunctionDef& fdef,
                       const std::unordered_set<string>& nodes) {
    ASSERT_EQ(nodes.size(), fdef.node_def_size())
        << "Got unexpected number of nodes. Expected: ["
        << absl::StrJoin(nodes, ", ")
        << "] Actual nodes in fdef: " << fdef.DebugString();
    for (const NodeDef& node_def : fdef.node_def()) {

Без контейнеров обеспечить запуск при старте и перезапуски может быть сложно. Но при **работе с контейнерами** в большинстве случаев этот функционал доступен по умолчанию. ✨

## Репликация — количество процессов { #replication-number-of-processes }

filegroup(
    name = "tensorflow_def_file",
    srcs = [":tf_custom_op_library_additional_deps.dll"],
    output_group = "def_file",
)

# Filter the DEF file to reduce the number of symbols to 64K or less.
# Note that we also write the name of the pyd file into DEF file so that
# the dynamic libraries of custom ops can find it at runtime.
genrule(
    name = "tensorflow_filtered_def_file",

}

func isDirObject(object string) bool {
	if obj := encodeDirObject(object); obj != object {
		object = obj
	}
	return HasSuffix(object, globalDirSuffix)
}

// Helper method to return total number of nodes in cluster
func totalNodeCount() int {
	totalNodesCount := len(globalEndpoints.Hostnames())
	if totalNodesCount == 0 {
		totalNodesCount = 1 // For standalone erasure coding
	}
	return totalNodesCount