// 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;

	// The probability distribution function is mean*exp(-mean*x), so the CDF is
	// p = 1 - exp(-mean*x), so
	// q = 1 - p == exp(-mean*x)
	// log_e(q) = -mean*x
	// -log_e(q)/mean = x
	// x = -log_e(q) * mean
	// x = log_2(q) * (-log_e(2)) * mean    ; Using log_2 for efficiency
	const randomBitCount = 26
	q := cheaprandn(1<<randomBitCount) + 1
	qlog := fastlog2(float64(q)) - randomBitCount
	if qlog > 0 {

	stackPoisonMin = uintptrMask & -4096
)

// Global pool of spans that have free stacks.
// Stacks are assigned an order according to size.
//
//	order = log_2(size/FixedStack)
//
// There is a free list for each order.
var stackpool [_NumStackOrders]struct {
	item stackpoolItem

(Div16 <t> n (Const16 [c])) && isPowerOfTwo16(c) =>
  (Rsh16x64
    (Add16 <t> n (Rsh16Ux64 <t> (Rsh16x64 <t> n (Const64 <typ.UInt64> [15])) (Const64 <typ.UInt64> [int64(16-log16(c))])))
    (Const64 <typ.UInt64> [int64(log16(c))]))
(Div32 <t> n (Const32 [c])) && isPowerOfTwo32(c) =>
  (Rsh32x64
    (Add32 <t> n (Rsh32Ux64 <t> (Rsh32x64 <t> n (Const64 <typ.UInt64> [31])) (Const64 <typ.UInt64> [int64(32-log32(c))])))

  func.return %0 : tensor<?xf32>
}

// CHECK-LABEL:   func @log1p(
// CHECK-SAME:                %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<2xf32> {
// CHECK:           %[[VAL_1:.*]] = "tf.Log1p"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>
// CHECK:           return %[[VAL_1]] : tensor<2xf32>
// CHECK:         }
func.func @log1p(%arg0: tensor<2xf32>) -> tensor<2xf32> {

	// Make sure this stays in sync with the compiler's definition.
	count     int // # live cells == size of map.  Must be first (used by len() builtin)
	flags     uint8
	B         uint8  // log_2 of # of buckets (can hold up to loadFactor * 2^B items)
	noverflow uint16 // approximate number of overflow buckets; see incrnoverflow for details
	hash0     uint32 // hash seed

	return int64(ntz64(^x))
}

// logX returns logarithm of n base 2.
// n must be a positive power of 2 (isPowerOfTwoX returns true).
func log8(n int8) int64 {
	return int64(bits.Len8(uint8(n))) - 1
}
func log16(n int16) int64 {
	return int64(bits.Len16(uint16(n))) - 1
}
func log32(n int32) int64 {
	return int64(bits.Len32(uint32(n))) - 1
}
func log64(n int64) int64 {
	return int64(bits.Len64(uint64(n))) - 1
}

    }
  }];
}

def TF_Log1pOp : TF_Op<"Log1p", [Pure, TF_CwiseUnary, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes natural logarithm of (1 + x) element-wise.";

  let description = [{
I.e., \\(y = \log_e (1 + x)\\).

Example:

```python
x = tf.constant([0, 0.5, 1, 5])
tf.math.log1p(x) ==> [0., 0.4054651, 0.6931472, 1.7917595]
```
  }];

        `tf.atan2`, `tf.atanh`, `tf.cos`, `tf.cosh`, `tf.equal`, `tf.exp`,
        `tf.floor`, `tf.greater`, `tf.greater_equal`, `tf.less`,
        `tf.less_equal`, `tf.log`, `tf.logp1`, `tf.logical_and`,
        `tf.logical_not`, `tf.logical_or`, `tf.maximum`, `tf.minimum`,
        `tf.not_equal`, `tf.sin`, `tf.sinh`, `tf.tan`
    *   Deprecate `tf.data.Dataset.shard`.