// If 'split_dim' is not a constant, there are no other checks.
  std::optional<int64_t> split_dim_opt =
      ExtractConstantIntFromTensor(op.getSplitDim());
  if (!split_dim_opt) return success();

  // If 'input' is not a ranked tensor, there are no other checks.
  auto input_type = op.getValue().getType().dyn_cast<RankedTensorType>();

        self._input_saved_model_path,
        self._output_saved_model_path,
        quantization_options,
    )
    self.assertIsNotNone(converted_model)
    # Due to other meta data, the compression is not exactly 1/4.
    self.assertLess(
        testing.get_size_ratio(
            self._output_saved_model_path, self._input_saved_model_path
        ),
        0.5,
    )

	if len(mfiles) > 0 {
		cgoLDFLAGS = append(cgoLDFLAGS, "-lobjc")
	}

	// Likewise for Fortran, except there are many Fortran compilers.
	// Support gfortran out of the box and let others pass the correct link options
	// via CGO_LDFLAGS
	if len(ffiles) > 0 {
		fc := cfg.Getenv("FC")
		if fc == "" {
			fc = "gfortran"
		}

// tf.Mul op over it and a scalar const tensor 1.0. For now this only applies to
// float32 tensors.
// TODO(hongm): Implement this op synthesis optimization for other dtypes if
// needed.
//
// Because coefficient-wise binary operations support implicit broadcasting, we
// should be very careful with this optimization, and do not accidentally

	local := globalLocalNodeName
	if local == "" {
		local = "127.0.0.1"
	}

	failedClients := make(map[int]struct{})

	if globalIsDistErasure {
		// Push binary to other servers
		for idx, nerr := range globalNotificationSys.VerifyBinary(ctx, u, sha256Sum, releaseInfo, binC) {
			if nerr.Err != nil {
				peerResults[nerr.Host.String()] = madmin.ServerPeerUpdateStatus{

// scalar value).
struct ValuePort {
  PointerUnion<Operation*, BlockArgument> producer;
  SmallVector<unsigned int, 2> port;

  bool operator==(const ValuePort& other) const {
    return producer == other.producer && port == other.port;
  }

  ValuePort() = default;

  // Convert output value to ValuePort.
  explicit ValuePort(Value v) {
    OpResult opr = mlir::dyn_cast<OpResult>(v);
    if (opr) {

// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
// THE SOFTWARE.

package ld

import (
	"bytes"
	"cmd/internal/gcprog"
	"cmd/internal/objabi"
	"cmd/internal/sys"

}

// These ops are named NonMaxSuppressionV4 & NonMaxSuppressionV5 to be
// consistent with TensorFlow's naming. They are NOT 'versions' of NMS in the
// sense that one is an incremental change over the other.
// In reality NonMaxSuppressionV5 implements Soft Non Max Suppression and
// NonMaxSuppressionV4 performs hard NMS.

def TFL_NonMaxSuppressionV4Op : TFL_Op<"non_max_suppression_v4", [
  Pure,

	// changes and to update pod status. On the other hand, a shorter period
	// will cause more frequent relisting (e.g., container runtime operations),
	// leading to higher cpu usage.
	// Note that even though we set the period to 1s, the relisting itself can
	// take more than 1s to finish if the container runtime responds slowly
	// and/or when there are many container changes in one cycle.

							// But, the function continues to run other hints because the Pod has PendingPlugins, which can result in queueImmediately.
							QueueingHintFn: queueHintReturnQueue,
						},
						{
							PluginName: "fooPlugin2",
							// It's interpreted as queueImmediately.
							// The function doesn't run other hints because queueImmediately is the highest priority.