//
  // Users of ClusterTrustBundles, including Kubelet, are free to reorder and
  // deduplicate certificate blocks in this file according to their own logic,
  // as well as to drop PEM block headers and inter-block data.
  optional string trustBundle = 2;

func convT64(val uint64) unsafe.Pointer
func convTstring(val string) unsafe.Pointer
func convTslice(val []uint8) unsafe.Pointer

// interface type assertions x.(T)
func assertE2I(inter *byte, typ *byte) *byte
func assertE2I2(inter *byte, typ *byte) *byte
func panicdottypeE(have, want, iface *byte)
func panicdottypeI(have, want, iface *byte)
func panicnildottype(want *byte)
func typeAssert(s *byte, typ *byte) *byte

	"github.com/minio/minio/internal/logger"
	"github.com/minio/pkg/v3/policy"
)

const (
	jwtAlgorithm = "Bearer"

	// Default JWT token for web handlers is one day.
	defaultJWTExpiry = 24 * time.Hour

	// Inter-node JWT token expiry is 15 minutes.
	defaultInterNodeJWTExpiry = 15 * time.Minute
)

var (
	errInvalidAccessKeyID = errors.New("The access key ID you provided does not exist in our records")

    /**
     * Indicates whether the model building should happen in two phases. If enabled, the initial invocation of the model
     * builder will only produce an interim result which may be used to analyze inter-model dependencies before the
     * final invocation of the model builder is performed.
     *
     * @return {@code true} if two-phase building is enabled, {@code false} if the model should be build in a single

  let summary = "Moves a broadcast from host into XLA, encoded as XlaAllReduce";

  let description = [{
    This pass moves brodcasts from host TF ops into XLA. This enables use of
    the inter-device network, which is faster than the inter-host network.
    Broadcasts in XLA are encoded as XlaAllReduce. An all_reduce with all 0
    inputs except for one real input produces the same result as a broadcast.

    For example, the following:

    : intra_op_(std::make_unique<IntraOpThreadPool>(num_threads)) {
  // Create a HostContext for running TFRT functions. Concurrent work queue acts
  // similar to the Tensorflow `inter-op` thread pool, so we'll match the size.
  host_context_ = num_threads ? CreateMultiThreadedHostContext(num_threads)
                              : CreateSingleThreadedHostContext();

	"reflect"
	"runtime"
	"testing"
	"time"

	"github.com/minio/minio/internal/grid"
	xnet "github.com/minio/pkg/v3/net"
)

// Storage REST server, storageRESTReceiver and StorageRESTClient are
// inter-dependent, below test functions are sufficient to test all of them.
func testStorageAPIDiskInfo(t *testing.T, storage StorageAPI) {
	testCases := []struct {
		expectErr bool
	}{
		{true},
	}

		interfaces, _ := net.Interfaces() //TODO create interfaces
		for _, inter := range interfaces {
			addresses, _ := inter.Addrs()
			for _, addr := range addresses {
				addrIP, _, _ := netutils.ParseCIDRSloppy(addr.String())
				if addrIP.String() == nodeIP.String() {
					klog.V(2).InfoS("Record Host MAC address", "addr", inter.HardwareAddr)
					hostMac = inter.HardwareAddr.String()
				}
			}
		}
		if len(hostMac) == 0 {

    // arguments must correspond to different resources and we can assign unique
    // IDs.
    assign_unique_id_to_all(func_op.getArguments());
  }

  // Since this analysis is neither inter-procedural nor inter-regional,
  // each region attached to Op's within a function is analyzed independently.
  // Seed this analysis for each such region by mapping all resource arguments

You will also find it easier to take advantage of Gradle's power features to improve build performance.
+
The main downside is the extra work required to perform the migration, particularly if the existing build is complex and has many inter-project dependencies.
However, these builds often benefit the most from a switch to idiomatic Gradle.