_, err := rand.Read(b)
		if err != nil {
			panic(fmt.Sprintf("rand.Read failed: %v", err))
		}
		// and turn it into a float64
		x := math.Float64frombits(binary.LittleEndian.Uint64(b))
		if math.IsNaN(x) || math.IsInf(x, 0) {
			continue
		}
		x = math.Abs(x)
		if x < 0x1p-1000 { // avoid underflow patterns
			continue
		}
		frac, _ := math.Frexp(x) // 52 bits of randomness
		return frac*2 - 1
	}

}

// NewFloat allocates and returns a new [Float] set to x,
// with precision 53 and rounding mode [ToNearestEven].
// NewFloat panics with [ErrNaN] if x is a NaN.
func NewFloat(x float64) *Float {
	if math.IsNaN(x) {
		panic(ErrNaN{"NewFloat(NaN)"})
	}
	return new(Float).SetFloat64(x)
}

// Exponent and precision limits.
const (
	MaxExp  = math.MaxInt32  // largest supported exponent

// of the mantissa. It will panic if the truncation results in lost information.
func truncate64Fto32F(f float64) float32 {
	if !isExactFloat32(f) {
		panic("truncate64Fto32F: truncation is not exact")
	}
	if !math.IsNaN(f) {
		return float32(f)
	}
	// NaN bit patterns aren't necessarily preserved across conversion
	// instructions so we need to do the conversion manually.
	b := math.Float64bits(f)

(FMOVDstore [off] {sym} ptr (MTVSRD x) mem) => (MOVDstore [off] {sym} ptr x mem)
(MOVDstore [off] {sym} ptr (MFVSRD x) mem) => (FMOVDstore [off] {sym} ptr x mem)

(MTVSRD (MOVDconst [c])) && !math.IsNaN(math.Float64frombits(uint64(c))) => (FMOVDconst [math.Float64frombits(uint64(c))])
(MFVSRD (FMOVDconst [c])) => (MOVDconst [int64(math.Float64bits(c))])

	b := v.Block
	typ := &b.Func.Config.Types
	// match: (MTVSRD (MOVDconst [c]))
	// cond: !math.IsNaN(math.Float64frombits(uint64(c)))
	// result: (FMOVDconst [math.Float64frombits(uint64(c))])
	for {
		if v_0.Op != OpPPC64MOVDconst {
			break
		}
		c := auxIntToInt64(v_0.AuxInt)
		if !(!math.IsNaN(math.Float64frombits(uint64(c)))) {
			break
		}
		v.reset(OpPPC64FMOVDconst)

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_IsNanOp : TF_Op<"IsNan", [Pure, SameOperandsAndResultShape]> {
  let summary = "Returns which elements of x are NaN.";

  let description = [{
@compatibility(numpy)
Equivalent to np.isnan
@end_compatibility

Example:

```python
x = tf.constant([5.0, np.nan, 6.8, np.nan, np.inf])

    *   Extend `tf.function` with basic support for CompositeTensors arguments
        (such as `SparseTensor` and `RaggedTensor`).
    *   `parallel_for.pfor`: add converters for Softmax, LogSoftmax, IsNaN, All,
        Any, and MatrixSetDiag.
    *   `parallel_for`: add converters for LowerTriangularSolve and Cholesky.
    *   `parallel_for`: add converters for `LogMatrixDeterminant` and
        `MatrixBandPart`.