}

  TF_Operation* UseT(const std::vector<TF_Output>& inputs) {
    TF_Operation* op;
    UseHelper(inputs, &op);
    return op;
  }

  // All the *Helper methods are used as a workaround for the restrictions that
  // one cannot call ASSERT_* methods in non-void-returning functions (when
  // exceptions are disabled during compilation)
  void UseHelper(const std::vector<TF_Output>& inputs, TF_Operation** op) {

# https://github.com/tensorflow/tensorflow/blob/cd67f4f3723f9165aabedd0171aaadc6290636e5/tensorflow/tensorflow.bzl#L396-L425
# And is usable in the "deps" attribute instead of the "srcs" attribute
# as a workaround for https://github.com/tensorflow/tensorflow/issues/34117
cc_import(
    name = "libtensorflow_framework_import_lib",
    shared_library = select({
        "//tensorflow:macos": ":libtensorflow_framework.dylib",

 * publicly visible subclass), and I doubt any of our current or future Kotlin extensions for the
 * package will refer to the class. Plus, @ParametricNullness is only a temporary workaround,
 * anyway, so we just need to get by without the annotations here until Kotlin better understands
 * our other nullness annotations.
 */
final class Synchronized {
  private Synchronized() {}

 * publicly visible subclass), and I doubt any of our current or future Kotlin extensions for the
 * package will refer to the class. Plus, @ParametricNullness is only a temporary workaround,
 * anyway, so we just need to get by without the annotations here until Kotlin better understands
 * our other nullness annotations.
 */
final class Synchronized {
  private Synchronized() {}

		// and throw a 404 exception. This is especially a problem for spark jobs overwriting the same partition
		// repeatedly. This workaround recursively lists the top 3 entries including delete markers to reflect the
		// correct state of the directory in the list results.
		if strings.HasSuffix(opts.Prefix, SlashSeparator) {