* </p>
 *
 * <p>
 * A typical use of a provider is to pass values from one Gradle model element to another, e.g. from a project extension
 * to a task, or between tasks. Providers also allow expensive computations to be deferred until their value is actually
 * needed, usually at task execution time.
 * </p>
 *
 * <p>
 * There are a number of ways to create a {@link Provider} instance. Some common methods:
 * </p>
 *

    XlaComputation& computation) {
  xla::DebugOptions debug_options;
  TF_ASSIGN_OR_RETURN(auto hlo_module_config,
                      xla::HloModule::CreateModuleConfigFromProto(
                          computation.proto(), debug_options));
  TF_ASSIGN_OR_RETURN(
      std::unique_ptr<xla::HloModule> hlo_module,
      xla::HloModule::CreateFromProto(computation.proto(), hlo_module_config));

import static java.util.stream.Collectors.toSet;

/**
 * This factory performs normalization of exclude rules. This is the smartest
 * of all factories and is responsible for doing some basic algebra computations.
 * It shouldn't be too slow, or the whole chain will pay the price.
 */
public class NormalizingExcludeFactory extends DelegatingExcludeFactory {
    private final Intersections intersections;

    replicated TPU computation. The number of times a TPU computation is
    replicated is defined in the `tf.TPUReplicateMetadata` op (`num_replicas`
    attribute) and operand and result sizes of `tf.TPUReplicatedInput` and
    `tf.TPUReplicatedOutput` respectively must match, excluding packed tensors.
    It is also assumed ops of the same TPU computation do not have ops outside

			c = High
		}
	}

	// We store the results of the computations of the tie-breaker rules along
	// with the best match. There is no need to do the checks once we determine
	// we have a winner, but we do still need to do the tie-breaker computations.
	// We use "beaten" to keep track if we still need to do the checks.

#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/util/command_line_flags.h"

namespace tensorflow {

struct XlaAutoJitFlag {
  // Control compilation of operators into XLA computations on CPU and GPU
  // devices.  0 = use ConfigProto setting; -1 = off; 1 = on for things very
  // likely to be improved; 2 = on for everything.
  //

		if err != nil {
			t.Fatal(err)
		}
		aliceSecret, err := aliceKey.ECDH(bobKey.PublicKey())
		if err != nil {
			t.Fatal(err)
		}

		if !bytes.Equal(bobSecret, aliceSecret) {
			t.Error("two ECDH computations came out different")
		}
	})
}

type countingReader struct {
	r io.Reader
	n int
}

func (r *countingReader) Read(p []byte) (int, error) {
	n, err := r.r.Read(p)
	r.n += n
	return n, err

void AppendMarkForCompilationPassFlagsInternal(std::vector<Flag>* flag_list) {
  std::vector<Flag> new_flags = {
      Flag("tf_xla_auto_jit", SetterForXlaAutoJitFlag, "0",
           "Control compilation of operators into XLA computations on CPU and "
           "GPU devices.  0 = use ConfigProto setting; -1 = off; 1 = on for "
           "things very likely to be improved; 2 = on for everything; "

	// 	x1:x0 := r.Uint64()
	// 	0:hi, lo1:lo0 := bits.Mul64(x1:x0, 0:n)
	// Writing out the multiplication in terms of bits.Mul32 allows
	// using direct hardware instructions and avoiding
	// the computations involving these zeros.
	x := r.Uint64()
	lo1a, lo0 := bits.Mul32(uint32(x), n)
	hi, lo1b := bits.Mul32(uint32(x>>32), n)
	lo1, c := bits.Add32(lo1a, lo1b, 0)
	hi += c
	if lo1 == 0 && lo0 < uint32(n) {

	PAND POLY, T0
	PSRLL $31, T1
	PSLLDQ $4, T1
	PSLLL $1, B0
	PXOR T0, B0
	PXOR T1, B0
	// Karatsuba pre-computations
	MOVOU B0, (16*14)(dst)
	PSHUFD $78, B0, B1
	PXOR B0, B1
	MOVOU B1, (16*15)(dst)

	MOVOU B0, B2
	MOVOU B1, B3
	// Now prepare powers of H and pre-computations for them
	MOVQ $7, AX

initLoop:
		MOVOU B2, T0
		MOVOU B2, T1
		MOVOU B3, T2
		PCLMULQDQ $0x00, B0, T0