licenses = ["notice"],
)

# Currently pybind extension shared objects must use only C API headers since
# the C API has static initializers duplicated in the Python bindings. So we
# need a second rule that omits .cc files, in
# tensorflow/python:_pywrap_parallel_device.
filegroup(
    name = "lib_headers",
    srcs = ["parallel_device_lib.h"],
)

filegroup(
    name = "lib_sources",

		// OPTIONAL. Authorized party - the party to which the ID
		// Token was issued. If present, it MUST contain the OAuth
		// 2.0 Client ID of this party. This Claim is only needed
		// when the ID Token has a single audience value and that
		// audience is different than the authorized party. It MAY
		// be included even when the authorized party is the same

	if dataBlocks+parityBlocks > 256 {
		return e, reedsolomon.ErrMaxShardNum
	}

	e = Erasure{
		dataBlocks:   dataBlocks,
		parityBlocks: parityBlocks,
		blockSize:    blockSize,
	}

	// Encoder when needed.
	var enc reedsolomon.Encoder
	var once sync.Once
	e.encoder = func() reedsolomon.Encoder {
		once.Do(func() {
			e, err := reedsolomon.New(dataBlocks, parityBlocks, reedsolomon.WithAutoGoroutines(int(e.ShardSize())))

            if (addedToBuffer && arg.endsWith("\"")) {
                // if we're building an argument, keep doing so.
                // if this is the case of "-Dfoo=bar", then we need to adjust the buffer.
                if (!currentArg.isEmpty()) {
                    currentArg.setLength(currentArg.length() - 1);
                }

                cleaned.add(currentArg.toString());

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual absl::Status InputLength(const char* input_name, int* length) = 0;

    * Add `compute_sparse_core_stats` for sparse core users to profile the  data with this API to get the `max_ids` and `max_unique_ids`. These numbers will be needed to configure the sparse core embedding mid level api.
    * Remove the `preprocess_features` method since that's no longer needed.

## Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

   * a duration in order to call a legacy API which requires a {@code long, TimeUnit} pair.
   */
  @SuppressWarnings({
    // We use this method only for cases in which we need to decompose to primitives.
    "GoodTime-ApiWithNumericTimeUnit",
    "GoodTime-DecomposeToPrimitive",
    // We use this method only from within APIs that require a Duration.
    "Java7ApiChecker",
  })

abstract class GwtFuturesCatchingSpecialization {
  /*
   * In the GWT versions of the methods (below), every exceptionType parameter is required to be
   * Class<Throwable>. To handle only certain kinds of exceptions under GWT, you'll need to write
   * your own instanceof tests.
   */

  public static <V extends @Nullable Object> ListenableFuture<V> catching(
      ListenableFuture<? extends V> input,
      Class<Throwable> exceptionType,

    fun CompilationUnit.toVersionIncubating(sourceFile: File, node: Node) =
        Pair(
            (node as? NodeWithJavadoc<*>)?.javadoc?.orElse(null)?.let { findVersionFromJavadoc(it) }
                // This is needed to find the JavaDoc of a package declaration in 'package-info.java'

setattr(_current_module, "metrics", _metrics)
setattr(_current_module, "optimizers", _optimizers)
setattr(_current_module, "initializers", _initializers)


# Do an eager load for Keras' code so that any function/method that needs to
# happen at load time will trigger, eg registration of optimizers in the
# SavedModel registry.
# See b/196254385 for more details.
try:
  if _tf_uses_legacy_keras: