//go:build !fips
// +build !fips

package sha256

import (
	"hash"

	nofipssha256 "github.com/minio/sha256-simd"
)

// New returns a new hash.Hash computing the SHA256 checksum.
// The SHA256 implementation is not FIPS 140-2 compliant.
func New() hash.Hash { return nofipssha256.New() }

// Sum256 returns the SHA256 checksum of the data.

     * is an ArrayList of SIDs represents the local groups that the account is
     * a member of.
     * <p/>
     * This method is designed to assist with computing access control for a
     * given user when the target object's ACL has local groups. Local groups
     * are not listed in a user's group membership (e.g. as represented by the

  // `gradient_checker` only works with model that returns only 1 tensor.
  // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the
  // second tensor isn't needed for computing gradient so we could safely drop
  // it.
  outputs[0] = loss;
  backprop->Unref();
  return absl::OkStatus();
}

Status BiasAddModel(AbstractContext* ctx,

import com.google.caliper.Param;
import com.google.caliper.api.SkipThisScenarioException;
import com.google.common.primitives.Doubles;
import java.util.Random;

/**
 * Benchmarks for various algorithms for computing the mean and/or variance.
 *
 * @author Louis Wasserman
 */
public class StatsBenchmark {

  enum MeanAlgorithm {
    SIMPLE {
      @Override
      double mean(double[] values) {

  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.

    assertThrows(UncheckedTimeoutException.class, () -> proxy.sleepThenReturnInput("x"));

    assertThat(stopwatch.elapsed(MILLISECONDS)).isIn(Range.closed(NOT_ENOUGH_MS, DELAY_MS * 2));
    // Is it still computing away anyway?
    assertThat(target.finished).isFalse();
    MILLISECONDS.sleep(ENOUGH_MS);
    assertThat(target.finished).isFalse();
  }

  public void testNewProxy_badMethodWithEnoughTime() throws Exception {

      Status s =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
      ASSERT_EQ(errors::OK, s.code()) << s.message();
      ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

 * will be constant across algorithms).
 *
 * @author Pete Gillin
 * @since 20.0
 */
enum QuantilesAlgorithm {

  /**
   * Sorts the dataset, and picks values from it. When computing multiple quantiles, we sort once
   * and pick multiple values.
   */
  SORTING {

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      Arrays.sort(dataset);

 * will be constant across algorithms).
 *
 * @author Pete Gillin
 * @since 20.0
 */
enum QuantilesAlgorithm {

  /**
   * Sorts the dataset, and picks values from it. When computing multiple quantiles, we sort once
   * and pick multiple values.
   */
  SORTING {

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      Arrays.sort(dataset);

  }

  public void testExpandDuringLoading() throws InterruptedException {
    final int count = 3;
    final AtomicInteger callCount = new AtomicInteger();
    // tells the computing thread when to start computing
    final CountDownLatch computeSignal = new CountDownLatch(1);
    // tells the main thread when computation is pending
    final CountDownLatch secondSignal = new CountDownLatch(1);