@IgnoreJRERequirement // Users will use this only if they're already using Duration
  public static <E> int drain(
      BlockingQueue<E> q, Collection<? super E> buffer, int numElements, Duration timeout)
      throws InterruptedException {
    // TODO(b/126049426): Consider using saturateToNanos(timeout) instead.
    return drain(q, buffer, numElements, timeout.toNanos(), NANOSECONDS);
  }

  /**

        requireNonNull(level, "level");
        requireNonNull(message, "message");
        entries.get().add(new Entry(level, message, error));
    }

    @Override
    public List<Entry> drain() {
        return entries.getAndSet(new CopyOnWriteArrayList<>());
    }

# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",
        "//learning/brain/keras/...",
        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",

<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li>
<li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li>
<li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li>
</ul>

# Decommissioning

Decommissiong is a mechanism in MinIO to drain older pools (usually with old hardware) and migrate the content from such pools to a newer pools (usually better hardware). Decommissioning spreads the data across all pools - for example, if you decommission `pool1`, all the data from `pool1` spreads across `pool2` and `pool3`.

## Features

Điều này trái ngược với các **giai đoạn phát triển**, trong đó bạn liên tục thay đổi mã, phá vỡ nó và sửa nó, ngừng và khởi động lại máy chủ phát triển, v.v.

## Các Chiến lược Triển khai

	// ReplicateQueued - replication being queued trail
	ReplicateQueued = "replicate:queue"

	// ReplicateExisting - audit trail for existing objects replication
	ReplicateExisting = "replicate:existing"
	// ReplicateExistingDelete - audit trail for delete replication triggered for existing delete markers
	ReplicateExistingDelete = "replicate:existing:delete"

	h          hash.Hash
	shardSize  int64
	hashBytes  []byte
}

func (b *streamingBitrotReader) Close() error {
	if b.rc == nil {
		return nil
	}
	if closer, ok := b.rc.(io.Closer); ok {
		// drain the body for connection reuse at network layer.
		xhttp.DrainBody(io.NopCloser(b.rc))
		return closer.Close()
	}
	return nil
}

func (b *streamingBitrotReader) ReadAt(buf []byte, offset int64) (int, error) {

# Tests

tf_cuda_library(
    name = "c_test_util",
    testonly = 1,
    srcs = ["c_test_util.cc"],
    hdrs = ["c_test_util.h"],
    visibility = [
        "//learning/brain:__subpackages__",
        "//tensorflow:__subpackages__",
    ],
    deps = [
        ":c_api",
        ":c_api_experimental",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",

* GCE: Prefer preconfigured node tags for firewalls, if available ([#25148](https://github.com/kubernetes/kubernetes/pull/25148), [@a-robinson](https://github.com/a-robinson))
* Drain pods created from ReplicaSets in 'kubectl drain' ([#23689](https://github.com/kubernetes/kubernetes/pull/23689), [@maclof](https://github.com/maclof))