}
  }

  private static double[] longsToDoubles(long[] longs) {
    int len = longs.length;
    double[] doubles = new double[len];
    for (int i = 0; i < len; i++) {
      doubles[i] = longs[i];
    }
    return doubles;
  }

  private static double[] intsToDoubles(int[] ints) {
    int len = ints.length;
    double[] doubles = new double[len];
    for (int i = 0; i < len; i++) {

      return LinearTransformation.vertical(xStats.mean());
    }
  }

  private double ensurePositive(double value) {
    if (value > 0.0) {
      return value;
    } else {
      return Double.MIN_VALUE;
    }
  }

  private static double ensureInUnitRange(double value) {
    return Doubles.constrainToRange(value, -1.0, 1.0);
  }

import jsinterop.annotations.JsPackage;

/** Web specializations for {@link Doubles} methods. */
public abstract class DoublesMethodsForWeb {

  @JsMethod(name = "Math.min", namespace = JsPackage.GLOBAL)
  public static native double min(double... array);

  @JsMethod(name = "Math.max", namespace = JsPackage.GLOBAL)
  public static native double max(double... array);

 * the License.
 */

package com.google.common.primitives;

import com.google.common.annotations.GwtCompatible;

/**
 * Holder for web specializations of methods of {@code Doubles}. Intended to be empty for regular
 * version.
 */
@GwtCompatible(emulated = true)
@ElementTypesAreNonnullByDefault

TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx,
                                         const tensorflow::tstring& value);

// Return a tensor handle containing a 2x2 matrix of doubles
TFE_TensorHandle* DoubleTestMatrixTensorHandle(TFE_Context* ctx);

// Return a tensor handle containing a 2x2 matrix of floats
TFE_TensorHandle* TestMatrixTensorHandle(TFE_Context* ctx);

      return LinearTransformation.vertical(xStats.mean());
    }
  }

  private double ensurePositive(double value) {
    if (value > 0.0) {
      return value;
    } else {
      return Double.MIN_VALUE;
    }
  }

  private static double ensureInUnitRange(double value) {
    return Doubles.constrainToRange(value, -1.0, 1.0);
  }

    for (double tolerance : TOLERANCE_CANDIDATES) {
      assertTrue(DoubleMath.fuzzyEquals(Double.NaN, Double.NaN, tolerance));
    }
  }

  public void testFuzzyEqualsZeroTolerance() {
    // make sure we test -0 tolerance
    for (double zero : Doubles.asList(0.0, -0.0)) {
      for (double a : ALL_DOUBLE_CANDIDATES) {
        for (double b : ALL_DOUBLE_CANDIDATES) {
          assertEquals(

 *
 * <ul>
 *   <li>{@link Booleans}
 *   <li>{@link Bytes}
 *       <ul>
 *         <li>{@link SignedBytes}
 *         <li>{@link UnsignedBytes}
 *       </ul>
 *   <li>{@link Chars}
 *   <li>{@link Doubles}
 *   <li>{@link Floats}
 *   <li>{@link Ints}
 *       <ul>
 *         <li>{@link UnsignedInts}
 *       </ul>
 *   <li>{@link Longs}
 *       <ul>
 *         <li>{@link UnsignedLongs}
 *       </ul>

    List<Integer> ints = Arrays.asList(0, 1, 2);
    List<Float> floats = Arrays.asList((float) 0, (float) 1, (float) 2);
    List<Long> longs = Arrays.asList((long) 0, (long) 1, (long) 2);
    List<Double> doubles = Arrays.asList((double) 0, (double) 1, (double) 2);

    assertThat(Bytes.toArray(bytes)).isEqualTo(array);
    assertThat(Bytes.toArray(shorts)).isEqualTo(array);
    assertThat(Bytes.toArray(ints)).isEqualTo(array);

    for (double tolerance : TOLERANCE_CANDIDATES) {
      assertTrue(DoubleMath.fuzzyEquals(Double.NaN, Double.NaN, tolerance));
    }
  }

  public void testFuzzyEqualsZeroTolerance() {
    // make sure we test -0 tolerance
    for (double zero : Doubles.asList(0.0, -0.0)) {
      for (double a : ALL_DOUBLE_CANDIDATES) {
        for (double b : ALL_DOUBLE_CANDIDATES) {
          assertEquals(