//
	i, locksFailed := 0, 0
	done := false

	for ; i < len(restClnts); i++ { // Loop until we acquired all locks
		select {
		case grant := <-ch:
			if grant.isLocked() {
				// Mark that this node has acquired the lock
				(*locks)[grant.index] = grant.lockUID
			} else {
				locksFailed++
				if locksFailed > tolerance {
					// We know that we are not going to get the lock anymore,

- [OEK](#oek): A secret and unique key used to encrypted the object, stored in an encrypted form as part of the object metadata and only loaded to RAM in plaintext during en/decrypting the object.
- [KEK](#kek): A secret and unique key used to en/decrypt the OEK and never stored anywhere. It is(re-)generated whenever en/decrypting an object using an external secret key and public parameters.

    /**
     * Provides a user-friendly hint about the location of the source. This could be a local file path, a URI or just an
     * empty string. The intention is to assist users during error reporting.
     *
     * @return A user-friendly hint about the location of the source, never {@code null}.
     */
    String getLocation();

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.execution;

/**
 * Defines events that Maven fires during a build. <strong>Warning:</strong> This interface might be extended in future
 * Maven versions to support further events. Hence it is strongly recommended to derive custom listeners from

   * resizing the table, as well as populating slots with new Cells.
   * There is no need for a blocking lock; when the lock is not
   * available, threads try other slots (or the base).  During these
   * retries, there is increased contention and reduced locality,
   * which is still better than alternatives.
   *
   * Per-thread hash codes are initialized to random values.

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized

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