# "//tensorflow/cc/experimental/tf2:__subpackages__",
        # copybara:uncomment_end
        "//tensorflow/compiler/mlir/tensorflow/c:__subpackages__",
        "//tensorflow/core/transforms:__subpackages__",
    ],
    deps = [
        ":c_api_macros_hdrs",
        "@local_xla//xla/tsl/c:tsl_status",
        "@local_xla//xla/tsl/c:tsl_status_internal",
        "@local_xla//xla/tsl/platform:status",

        assertEquals(1, stats.getRdmaSends(), "Should have 1 send operation");
        assertEquals(1, stats.getRdmaReceives(), "Should have 1 receive operation");
        assertEquals(5120, stats.getBytesTransferred(), "Should have transferred 5KB total");
        assertEquals(1, stats.getOperationErrors(), "Should have 1 error");
        assertEquals(0.25, stats.getErrorRate(), 0.001, "Error rate should be 25% (1 error out of 4 successful operations)");

        this.shutdownServer = shutdownServer
      }

    /**
     * Throttles the request reader and response writer to sleep for the given period after each
     * series of [bytesPerPeriod] bytes are transferred. Use this to simulate network behavior.
     */
    public fun throttleBody(
      bytesPerPeriod: Long,
      period: Long,
      unit: TimeUnit,
    ): Builder =
      apply {

//
// A leaf is only scanned once every dataUsageUpdateDirCycles,
// rarer if the bloom filter for the path is clean and no lifecycles are applied.
// Skipped leaves have their totals transferred from the previous cycle.
//
// When selected there is a one in healObjectSelectProb that any object will be chosen for heal scan.
//
// Compaction happens when either:
//

  private Iterable<LoadingCache<Object, Object>> caches() {
    // lots of different ways to configure a LoadingCache
    CacheBuilderFactory factory = cacheFactory();
    return Iterables.transform(
        factory.buildAllPermutations(),
        new Function<CacheBuilder<Object, Object>, LoadingCache<Object, Object>>() {
          @Override

        assertTrue(encrypted.length > plaintext.length, "Encrypted message should be larger than plaintext");

        // Verify transform header is present (first 52 bytes)
        assertTrue(encrypted.length >= 52, "Encrypted message should include transform header");

        // When - Decrypt
        byte[] decrypted = context.decryptMessage(encrypted);

        // Then - Verify decryption

    MEMORY_REGISTRATION, // Dynamic memory registration
    FAST_REGISTRATION   // Fast memory region registration
}

public class RdmaCapabilities {
    // RDMA transform capabilities
    public static final int SMB_DIRECT_RESPONSE_REQUESTED = 0x00000001;
    
    // Default RDMA settings
    public static final int DEFAULT_RDMA_READ_WRITE_SIZE = 1048576;  // 1MB

              }
            });
  }

  @J2ktIncompatible
  @GwtIncompatible // reflection
  private static FluentIterable<MediaType> getConstants() {
    return getConstantFields()
        .transform(
            new Function<Field, MediaType>() {
              @Override
              public MediaType apply(Field input) {
                try {
                  return (MediaType) input.get(null);

    }

    /**
     * Encrypt a message using the session's encryption context
     *
     * @param message the message to encrypt
     * @return encrypted message with transform header
     * @throws CIFSException if encryption fails or is not enabled
     */
    public byte[] encryptMessage(byte[] message) throws CIFSException {
        if (this.encryptionContext == null) {

    [following API review practices](https://github.com/tensorflow/community/blob/master/governance/api-reviews.md).
*   When you contribute a new feature to TensorFlow, the maintenance burden is
    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)