return tab
	}
	// Load the current cache.
	oldC := (*abi.TypeAssertCache)(atomic.Loadp(unsafe.Pointer(&s.Cache)))

	if cheaprand()&uint32(oldC.Mask) != 0 {
		// As cache gets larger, choose to update it less often
		// so we can amortize the cost of building a new cache.
		return tab
	}

	// Make a new cache.
	newC := buildTypeAssertCache(oldC, t, tab)

        val minSupertypeCount = matchingClassesRanking.maxOf { it.value }

        // If there are multiple matching classes, at least choose some stable one (based on the simple name ordering)
        return matchingClassesRanking
            .filter { it.value == minSupertypeCount }
            .keys
            .sortedBy { it.simpleName }

    # For Lightstep, always propagate the x-ot-span-context header.
    # For Datadog, propagate the corresponding datadog headers.
    # For OpenCensusAgent and Stackdriver configurations, you can choose any
    # set of compatible headers to propagate within your application. For
    # example, you can propagate b3 headers or W3C trace context headers with

  bool tf_xla_sparse_core_disable_table_stacking;

  // If non-zero, limits the size of the activations for a given table to
  // be below these many bytes.
  int64_t tf_xla_sparse_core_stacking_mem_limit_bytes;

  // If non-zero, limits the size of any table shard to be below these
  // many bytes.
  int64_t tf_xla_sparse_core_stacking_table_shard_limit_bytes;
};

// Flags associated with the XLA bridge's xla_device module.

// message containing all configurable options.
// Next ID: 9
message QuantizationConfig {
  // Config presets provide predefined popular or common quantization specs.
  // Lightweight users may choose one of the presets for quick experiments. Each
  // preset is completely represented by other fields in `QuantizationConfig`.
  //
  // When extra entries in `QuantizationSpecs` are provided along with a preset,

	rPrime := new(Element).Multiply(rr, sqrtM1) // r_prime = SQRT_M1 * r
	// r = CT_SELECT(r_prime IF flipped_sign_sqrt | flipped_sign_sqrt_i ELSE r)
	rr.Select(rPrime, rr, flippedSignSqrt|flippedSignSqrtI)

	r.Absolute(rr) // Choose the nonnegative square root.
	return r, correctSignSqrt | flippedSignSqrt

		ParentErrorNoHostname,
		ParentErrorParentRefConflict,
		// Failures to match the Port or SectionName. These are last so that if we bind to 1 listener we
		// just report errors for that 1 listener instead of for all sections we didn't bind to
		ParentErrorNotAccepted,
	}
	// Next we want to collapse these. We need to report 1 type of error, or none.
	report := map[k8s.ParentReference]RouteParentResult{}

}

// There are 2 standard tf ops which are not TensorList ops that may take as
// input a tensorlist. These are tf.AddN and tf.ZeroesLike. Since the runtime
// implementation of a tensorlist are not compatible between tf and tflite
// we cannot use tflite tensorlist kernels until these cases are handled.
bool IsNonTensorListVariantOp(Operation* op) {

				if endpointAddress == "" {
					endpointAddress = model.LocalhostIPv6AddressPrefix
				}
			} else if hostIP == model.LocalhostAddressPrefix {
				// prefer 127.0.0.1 to ::1, but if given no option choose ::1
				ipV6EndpointAddress := ""
				for _, host := range actualLocalHosts {
					if netutil.IsIPv4Address(host) {
						endpointAddress = host
						break
					}
					if netutil.IsIPv6Address(host) {

	{0x7d0a377f, 0xde2e65c5, "C is as portable as Stonehedge!!", "crc\x01ʇ\x91M\xf7\xd6\x00\xd5", "crc\x01wB\x84\x81de\\\xf8"},
	{0x8c79fd79, 0x297a88ed, "Even if I could be Shakespeare, I think I should still choose to be Faraday. - A. Huxley", "crc\x01ʇ\x91Ml+\xb8\xa7", "crc\x01wB\x84\x81\xbf\xd6S\xdd"},