*/
    return (int) Math.pow(a, 1.0 / concentration);
  }

  @AfterExperiment
  void tearDown() {
    double req = requests.get();
    double hit = req - misses.get();

    // Currently, this is going into /dev/null, but I'll fix that
    System.out.println("hit rate: " + hit / req);
  }

  // for proper distributions later:
  // import JSci.maths.statistics.ProbabilityDistribution;

     */
    return (int) Math.pow(a, 1.0 / concentration);
  }

  @AfterExperiment
  void tearDown() {
    double req = requests.get();
    double hit = req - misses.get();

    // Currently, this is going into /dev/null, but I'll fix that
    System.out.println("hit rate: " + hit / req);
  }

  // for proper distributions later:
  // import JSci.maths.statistics.ProbabilityDistribution;

    TFE_OpAddInput(matmul, h0_task0, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    TFE_OpAddInput(matmul, has_packed_input ? packed_handle : h1_task2, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  } else {
    // Handles are on task0 (local), and task2, but op is on task1.
    matmul = MatMulOp(ctx, h0_task0, h1_task2);
  }
  if (remote) {

        versionProperties["spock"] = "2.2-groovy-${groovyVersion.major}.${groovyVersion.minor}"

        findLatest("guava", "com.google.guava:guava:(20,)", versionProperties)
        findLatest("commons-math", "org.apache.commons:commons-math3:latest.release", versionProperties)
        findLatest("commons-text", "org.apache.commons:commons-text:latest.release", versionProperties)

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_Op* matmul = MatMulOp(ctx, h0_task1, h1_task1);
  TFE_OpSetDevice(matmul, remote_device_name, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

pkg math/rand/v2, func Shuffle(int, func(int, int)) #61716
pkg math/rand/v2, func Uint32() uint32 #61716
pkg math/rand/v2, func Uint32N(uint32) uint32 #61716
pkg math/rand/v2, func Uint64() uint64 #61716
pkg math/rand/v2, func Uint64N(uint64) uint64 #61716
pkg math/rand/v2, func UintN(uint) uint #61716
pkg math/rand/v2, method (*ChaCha8) MarshalBinary() ([]uint8, error) #61716
pkg math/rand/v2, method (*ChaCha8) Seed([32]uint8) #61716

==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_C_API_REMOTE_TEST_UTIL_H_
#define TENSORFLOW_C_EAGER_C_API_REMOTE_TEST_UTIL_H_

// Run a function containing a MatMul op and check its output.
// If heavy_load_on_streaming_rpc is true, send some rpc requests before the one
// which creates a remote input, to simulate a scenario that the remote input

		m.Set(sysCPULoadPerc, math.Round(perc*100)/100)
	}

	ts := cpuMetrics.TimesStat
	if ts != nil {
		tot := ts.User + ts.System + ts.Idle + ts.Iowait + ts.Nice + ts.Steal
		cpuUserVal := math.Round(ts.User/tot*100*100) / 100
		m.Set(sysCPUUser, cpuUserVal)
		cpuSystemVal := math.Round(ts.System/tot*100*100) / 100
		m.Set(sysCPUSystem, cpuSystemVal)
		cpuNiceVal := math.Round(ts.Nice/tot*100*100) / 100

 * limitations under the License.
 */

package com.google.common.math;

import static com.google.common.math.MathTesting.ALL_BIGINTEGER_CANDIDATES;
import static com.google.common.math.MathTesting.FINITE_DOUBLE_CANDIDATES;
import static com.google.common.math.MathTesting.POSITIVE_FINITE_DOUBLE_CANDIDATES;
import static com.google.common.truth.Truth.assertThat;

 * limitations under the License.
 */

package com.google.common.math;

import static com.google.common.math.MathBenchmarking.ARRAY_MASK;
import static com.google.common.math.MathBenchmarking.ARRAY_SIZE;
import static com.google.common.math.MathBenchmarking.randomNonZeroBigInteger;
import static com.google.common.math.MathBenchmarking.randomPositiveBigInteger;

import com.google.caliper.BeforeExperiment;