return CollectCollectors.toImmutableSortedMap(
        comparator, keyFunction, valueFunction, mergeFunction);
  }

  /*
   * TODO(kevinb): Confirm that ImmutableSortedMap is faster to construct and
   * uses less memory than TreeMap; then say so in the class Javadoc.
   */
  private static final Comparator<?> NATURAL_ORDER = Ordering.natural();

    return CollectCollectors.toImmutableSortedMap(
        comparator, keyFunction, valueFunction, mergeFunction);
  }

  /*
   * TODO(kevinb): Confirm that ImmutableSortedMap is faster to construct and
   * uses less memory than TreeMap; then say so in the class Javadoc.
   */
  private static final Comparator<?> NATURAL_ORDER = Ordering.natural();

 * asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, Function, Executor) Futures.transform}, but you will often
 * find it easier to use a framework. Frameworks automate the process, often adding features like
 * monitoring, debugging, and cancellation. Examples of frameworks include:
 *
 * <ul>

     * changing any bin. The operations must not take any action that could even momentarily
     * cause a concurrent read operation to see inconsistent data. This is made easier by the
     * nature of the read operations in Map. For example, no operation can reveal that the table
     * has grown but the threshold has not yet been updated, so there are no atomicity requirements

        ":with_tpu_support_define",
        ":with_tpu_support_flag",
    ],
    visibility = ["//visibility:public"],
)

# Specifies via a config setting if this is a mobile build or not, makes
# it easier to combine settings later.
selects.config_setting_group(
    name = "mobile",
    match_any = [
        ":android",
        ":chromiumos",

		// object SparkJSONRead {
		//   def main(args: Array[String]): Unit = {
		//     val spark:SparkSession = SparkSession.builder()
		//       .appName("SparkByExample")
		//       .master("local[1]").getOrCreate()
		//
		//     spark.sparkContext.setLogLevel("ERROR")
		//     spark.sparkContext.hadoopConfiguration.set("fs.s3a.endpoint", "http://minio-lb:9000")

 private:
  TF_Graph* graph_;
  int counter_;
};

// Helper macros for the TF_OperationGetAttr* tests.
// TODO(ashankar): Use gmock matchers instead?
// (https://github.com/google/googletest/blob/master/googlemock/docs/CookBook.md#writing-new-parameterized-matchers-quickly)
// That will require setting up the tensorflow build with gmock.
#define EXPECT_TF_META(attr_name, expected_list_size, expected_type, \