<PatternLayout><Pattern>${log.pattern}</Pattern></PatternLayout><!-- <EcsLayout serviceName="fess" eventDataset="crawler" /> -->
			<Policies>
				<TimeBasedTriggeringPolicy />
				<SizeBasedTriggeringPolicy size="100 MB" />
			</Policies>
			<DefaultRolloverStrategy fileIndex="max" min="1"
				max="${backup.max.history}" compressionLevel="9" />
		</RollingFile>
		<Rewrite name="AppFile">

			<PatternLayout><Pattern>${log.pattern}</Pattern></PatternLayout><!-- <EcsLayout serviceName="fess" eventDataset="app" /> -->
			<Policies>
				<TimeBasedTriggeringPolicy />
				<SizeBasedTriggeringPolicy size="100 MB" />
			</Policies>
			<DefaultRolloverStrategy fileIndex="max" min="1"
				max="${backup.max.history}" compressionLevel="9">
				<Delete basePath="${log.file.basedir}">

			<PatternLayout><Pattern>${log.pattern}</Pattern></PatternLayout><!-- <EcsLayout serviceName="fess" eventDataset="suggest" /> -->
			<Policies>
				<TimeBasedTriggeringPolicy />
				<SizeBasedTriggeringPolicy size="100 MB" />
			</Policies>
			<DefaultRolloverStrategy fileIndex="max" min="1"
				max="${backup.max.history}" compressionLevel="9" />
		</RollingFile>

			<PatternLayout><Pattern>${log.pattern}</Pattern></PatternLayout><!-- <EcsLayout serviceName="fess" eventDataset="thumbnail" /> -->
			<Policies>
				<TimeBasedTriggeringPolicy />
				<SizeBasedTriggeringPolicy size="100 MB" />
			</Policies>
			<DefaultRolloverStrategy fileIndex="max" min="1"
				max="${backup.max.history}" compressionLevel="9" />
		</RollingFile>

    for (int i = 1; i < numKeys; i++) {
      nodes.get(i).checkAcquiredLocks(Policies.THROW, nodes.subList(0, i));
    }
    // Pre-populate all disallowedPriorLocks with nodes of larger ordinal.
    for (int i = 0; i < numKeys - 1; i++) {
      nodes.get(i).checkAcquiredLocks(Policies.DISABLED, nodes.subList(i + 1, numKeys));
    }
    return Collections.unmodifiableMap(map);
  }

  /**

          * The experimental version of `Policy` optionally took in a `tf.compat.v1.mixed_precision.LossScale` in the constructor, which defaulted to a dynamic loss scale for the `"mixed_float16"` policy and no loss scale for other policies. In `Model.compile`, if the model's policy had a loss scale, the optimizer would be wrapped with a `LossScaleOptimizer`. With the non-experimental `Policy`, there is no loss scale associated with the `Policy`, and `Model.compile` wraps the optimizer...