*   Added warmup capabilities to `tf.keras.optimizers.schedules.CosineDecay` learning rate scheduler. You can now specify an initial and target learning rate, and our scheduler will perform a linear interpolation between the two after which it will begin a decay phase....

                            "clean performance:%testProject%PerformanceAdHocTest --tests \"%scenario%\"",
                            "%performance.baselines%",
                            """--warmups %warmups% --runs %runs% --checks %checks% --profiler %profiler% %generateDiffs% %additional.gradle.parameters%""",
                            os,
                            arch,
                            "%testJavaVersion%",

            defaultMap.put(FessConfig.LOG_NOTIFICATION_BUFFER_SIZE, "1000");
            defaultMap.put(FessConfig.LOG_NOTIFICATION_INTERVAL, "300");
            defaultMap.put(FessConfig.lasta_di_SMART_DEPLOY_MODE, "warm");
            defaultMap.put(FessConfig.DEVELOPMENT_HERE, "true");
            defaultMap.put(FessConfig.ENVIRONMENT_TITLE, "Local Development");
            defaultMap.put(FessConfig.FRAMEWORK_DEBUG, "false");