}

  // Depends on the correctness of BigIntegerMath.binomial.
  public void testBinomial() {
    for (int n = 0; n <= 70; n++) {
      for (int k = 0; k <= n; k++) {
        BigInteger expectedBig = BigIntegerMath.binomial(n, k);
        long expectedLong = fitsInLong(expectedBig) ? expectedBig.longValue() : Long.MAX_VALUE;
        assertEquals(expectedLong, LongMath.binomial(n, k));
      }
    }
  }

  }

  // Depends on the correctness of BigIntegerMath.binomial.
  public void testBinomial() {
    for (int n = 0; n <= 50; n++) {
      for (int k = 0; k <= n; k++) {
        BigInteger expectedBig = BigIntegerMath.binomial(n, k);
        int expectedInt = fitsInInt(expectedBig) ? expectedBig.intValue() : Integer.MAX_VALUE;
        assertEquals(expectedInt, IntMath.binomial(n, k));
      }
    }
  }

  static final Random RANDOM_SOURCE = new Random(314159265358979L);
  static final int MAX_EXPONENT = 100;

  /*
   * Duplicated from LongMath.
   * binomial(biggestBinomials[k], k) fits in a long, but not
   * binomial(biggestBinomials[k] + 1, k).
   */
  static final int[] biggestBinomials = {
    Integer.MAX_VALUE,
    Integer.MAX_VALUE,
    Integer.MAX_VALUE,
    3810779,
    121977,

  static final Random RANDOM_SOURCE = new Random(314159265358979L);
  static final int MAX_EXPONENT = 100;

  /*
   * Duplicated from LongMath.
   * binomial(biggestBinomials[k], k) fits in a long, but not
   * binomial(biggestBinomials[k] + 1, k).
   */
  static final int[] biggestBinomials = {
    Integer.MAX_VALUE,
    Integer.MAX_VALUE,
    Integer.MAX_VALUE,
    3810779,
    121977,

  };

  /**
   * Returns {@code n} choose {@code k}, also known as the binomial coefficient of {@code n} and
   * {@code k}, or {@link Long#MAX_VALUE} if the result does not fit in a {@code long}.
   *
   * @throws IllegalArgumentException if {@code n < 0}, {@code k < 0}, or {@code k > n}
   */
  public static long binomial(int n, int k) {
    checkNonNegative("n", n);
    checkNonNegative("k", k);

    *   Add a new "result_type" parameter to `tf.strings.split`.
    *   Add name argument to `tf.string_split` and `tf.strings_split`.
    *   Extend `tf.strings.split` to support inputs with any rank.
    *   Added `tf.random.binomial`.
    *   Added `key` and `skip` methods to `random.experimental.Generator`.
    *   Extend `tf.function` with basic support for CompositeTensors arguments
        (such as `SparseTensor` and `RaggedTensor`).