*
 * <p>This class is usually preferable to {@link AtomicLong} when multiple threads update a common
 * sum that is used for purposes such as collecting statistics, not for fine-grained synchronization
 * control. Under low update contention, the two classes have similar characteristics. But under
 * high contention, expected throughput of this class is significantly higher, at the expense of
 * higher space consumption.
 *

  // TF eager runtime.
  // TODO(b/172877902): These helper functions are currently used to support
  // PyFuncOp on TFRT, and might be useful for ops that directly use low
  // level TF APIs. Remove/replace the following functions when TFRT native
  // ops are implemented.
  //===--------------------------------------------------------------------===//

	}
	cfg.ListQuorum = listQuorum

	replicationPriority := env.Get(EnvAPIReplicationPriority, kvs.GetWithDefault(apiReplicationPriority, DefaultKVS))
	switch replicationPriority {
	case "slow", "fast", "auto":
	default:
		return cfg, fmt.Errorf("invalid value %v for replication_priority", replicationPriority)
	}
	cfg.ReplicationPriority = replicationPriority

        localhostIpv6,
      )

    client =
      clientTestRule.newClientBuilder()
        .eventListenerFactory(clientTestRule.wrap(listener))
        .connectTimeout(60, TimeUnit.SECONDS) // Deliberately exacerbate slow fallbacks.
        .dns { dnsResults }
        .fastFallback(true)
        .build()
    url =
      serverIpv4.url("/")
        .newBuilder()
        .host("localhost")
        .build()
  }

        blocker = (Blocker) state;
      }
      spinCount++;
      if (spinCount > MAX_BUSY_WAIT_SPINS) {
        /*
         * If we have spun a lot, just park ourselves. This will save CPU while we wait for a slow
         * interrupting thread. In theory, interruptTask() should be very fast, but due to
         * InterruptibleChannel and JavaLangAccess.blockedOn(Thread, Interruptible), it isn't

 *
 * <p>This class is usually preferable to {@link AtomicLong} when multiple threads update a common
 * sum that is used for purposes such as collecting statistics, not for fine-grained synchronization
 * control. Under low update contention, the two classes have similar characteristics. But under
 * high contention, expected throughput of this class is significantly higher, at the expense of
 * higher space consumption.
 *

functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the

specific to each architecture.
The sections below list these.
</p>

<p>
One detail evident in the examples from the previous sections is that data in the instructions flows from left to right:
<code>MOVQ</code> <code>$0,</code> <code>CX</code> clears <code>CX</code>.
This rule applies even on architectures where the conventional notation uses the opposite direction.
</p>

<p>

			}

			const setInt32 = (addr, v) => {
				this.mem.setUint32(addr + 0, v, true);
			}

			const getInt64 = (addr) => {
				const low = this.mem.getUint32(addr + 0, true);
				const high = this.mem.getInt32(addr + 4, true);
				return low + high * 4294967296;
			}

			const loadValue = (addr) => {
				const f = this.mem.getFloat64(addr, true);
				if (f === 0) {
					return undefined;
				}

   *       </ol>
   *       Given all these possibilities, it is frequently possible for listeners to execute in UI
   *       threads, RPC network threads, or other latency-sensitive threads. In those cases, slow
   *       listeners can harm responsiveness, slow the system as a whole, or worse. (See also the
   *       note about locking below.)
   *   <li>If many tasks will be triggered by the same event, one heavyweight task may delay other