absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(vnvo2409): Add control dependency
    /* Given upstream grad U and a Log1p op: Y = log(1 + X), the gradients are:
     *
     *    dX = U / (1 + X)
     *
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    AbstractTensorHandle* X = forward_inputs_[0];

     * is close to 1 (why?), gives the following formula.
     */
    double fractionOfBitsSet = (double) bitCount / bitSize;
    return DoubleMath.roundToLong(
        -Math.log1p(-fractionOfBitsSet) * bitSize / numHashFunctions, RoundingMode.HALF_UP);
  }

  /** Returns the number of bits in the underlying bit array. */
  @VisibleForTesting
  long bitSize() {
    return bits.bitSize();

     * is close to 1 (why?), gives the following formula.
     */
    double fractionOfBitsSet = (double) bitCount / bitSize;
    return DoubleMath.roundToLong(
        -Math.log1p(-fractionOfBitsSet) * bitSize / numHashFunctions, RoundingMode.HALF_UP);
  }

  /** Returns the number of bits in the underlying bit array. */
  @VisibleForTesting
  long bitSize() {
    return bits.bitSize();

        assertEquals(BigIntegerMath.log10(valueOf(x), mode), LongMath.log10(x, mode));
      }
    }
  }

  // Relies on the correctness of log10(long, FLOOR) and of pow(long, int).
  @GwtIncompatible // TODO
  public void testLog10Exact() {
    for (long x : POSITIVE_LONG_CANDIDATES) {
      int floor = LongMath.log10(x, FLOOR);
      boolean expectedSuccess = LongMath.pow(10, floor) == x;

  // Relies on the correctness of log10(BigInteger, {HALF_UP,HALF_DOWN}).
  @GwtIncompatible // TODO
  public void testLog10HalfEven() {
    for (BigInteger x : POSITIVE_BIGINTEGER_CANDIDATES) {
      int halfEven = BigIntegerMath.log10(x, HALF_EVEN);
      // Now figure out what rounding mode we should behave like (it depends if FLOOR was
      // odd/even).
      boolean floorWasEven = (BigIntegerMath.log10(x, FLOOR) & 1) == 0;

        assertEquals(BigIntegerMath.log10(valueOf(x), mode), LongMath.log10(x, mode));
      }
    }
  }

  // Relies on the correctness of log10(long, FLOOR) and of pow(long, int).
  @GwtIncompatible // TODO
  public void testLog10Exact() {
    for (long x : POSITIVE_LONG_CANDIDATES) {
      int floor = LongMath.log10(x, FLOOR);
      boolean expectedSuccess = LongMath.pow(10, floor) == x;

                .missing(0.1f).modifier(FieldValueFactorFunction.Modifier.LOG2P).setWeight(1.0F)));
        flist.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(ScoreFunctionBuilders.fieldValueFactorFunction(FieldNames.QUERY_FREQ)
                .missing(0.1f).modifier(FieldValueFactorFunction.Modifier.LOG2P).setWeight(1.0F)));
        flist.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(

      (TF_TensorScatterAddOp
       (TF_FillOp $shape, (TF_ConstOp (GetScalarOfType<0> $updates))),
       $indices, $updates)>;

//===----------------------------------------------------------------------===//
// Xdivy, Xlog1p and Xlogy op patterns.
//===----------------------------------------------------------------------===//

class BinaryXopyPat<dag From, dag To> : Pat<
  From,
  (TF_SelectV2Op
    (TF_EqualOp
      $x,

        TypeID::get<TF::LeftShiftOp>(),
        TypeID::get<TF::LessOp>(),
        TypeID::get<TF::ListDiffOp>(),
        TypeID::get<TF::LogicalAndOp>(),
        TypeID::get<TF::LogicalNotOp>(),
        TypeID::get<TF::LogOp>(),
        TypeID::get<TF::LowerBoundOp>(),
        TypeID::get<TF::MakeUniqueOp>(),
        TypeID::get<TF::MatMulOp>(),
        TypeID::get<TF::MatrixDiagV3Op>(),
        TypeID::get<TF::MatrixInverseOp>(),