/** The key of the message: Result */
    public static final String LABELS_SCRIPT_RESULT = "{labels.scriptResult}";

    /** The key of the message: Executor */
    public static final String LABELS_SCRIPT_TYPE = "{labels.scriptType}";

    /** The key of the message: Segmentation */
    public static final String LABELS_SEGMENTATION = "{labels.segmentation}";

# Make Bazel not try to probe the host system for a C++ toolchain.
build:rbe_base --config=resultstore
build:rbe_base --repo_env=BAZEL_DO_NOT_DETECT_CPP_TOOLCHAIN=1
build:rbe_base --define=EXECUTOR=remote
build:rbe_base --jobs=800
build:rbe_base --remote_executor=grpcs://remotebuildexecution.googleapis.com
build:rbe_base --remote_timeout=3600
build:rbe_base --spawn_strategy=remote,worker,standalone,local

job.template.title.data=Data Crawler - {0}
job.template.script=return container.getComponent("crawlJob").logLevel("info").webConfigIds([{0}] as String[]).fileConfigIds([{1}] as String[]).dataConfigIds([{2}] as String[]).jobExecutor(executor).execute();
job.max.crawler.processes=0
job.default.script=groovy
job.system.property.filter.pattern=

processors=0
java.command.path=java
python.command.path=python
path.encoding=UTF-8

    visibility = ["//visibility:public"],
    win_def_file = ":tensorflow_filtered_def_file",
)

# To avoid duplication, check that the C++ or python library does not depend on
# the stream executor cuda plugins. Targets that want to use cuda APIs should
# instead depend on the dummy plugins in @local_tsl//tsl/platform/default/build_config
# and use header only targets.

    /** The key of the configuration. e.g. return container.getComponent("crawlJob").logLevel("info").webConfigIds([{0}] as String[]).fileConfigIds([{1}] as String[]).dataConfigIds([{2}] as String[]).jobExecutor(executor).execute(); */
    String JOB_TEMPLATE_SCRIPT = "job.template.script";

    /** The key of the configuration. e.g. 0 */
    String JOB_MAX_CRAWLER_PROCESSES = "job.max.crawler.processes";

        decisions.
*   PluggableDevice
    *   Third-party devices can now connect to TensorFlow as plug-ins through
        [StreamExecutor C API](https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api.md).
        and
        [PluggableDevice](https://github.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md)
        interface.
        *   Add custom ops and kernels through