// result: (FMOVDfpgp <t> (VUADDLV <typ.Float64> (VCNT <typ.Float64> (FMOVDgpfp <typ.Float64> (ZeroExt16to64 x)))))
	for {
		t := v.Type
		x := v_0
		v.reset(OpARM64FMOVDfpgp)
		v.Type = t
		v0 := b.NewValue0(v.Pos, OpARM64VUADDLV, typ.Float64)
		v1 := b.NewValue0(v.Pos, OpARM64VCNT, typ.Float64)
		v2 := b.NewValue0(v.Pos, OpARM64FMOVDgpfp, typ.Float64)
		v3 := b.NewValue0(v.Pos, OpZeroExt16to64, typ.UInt64)

        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with
        `tf.keras.backend.set_floatx('float64')`, or pass `dtype='float64'` to

    Arg<TF_Int32Tensor, [{int32 `Tensor`.}]>:$arr,
    Arg<TF_Int32Tensor, [{non-negative int32 scalar `Tensor`.}]>:$size,
    Arg<TensorOf<[TF_Float32, TF_Float64, TF_Int32, TF_Int64]>, [{is an int32, int64, float32, or float64 `Tensor` with the same
shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights
equal to 1.}]>:$weights
  );

  let results = (outs

func rewriteValuegeneric_OpCvt32Fto64F(v *Value) bool {
	v_0 := v.Args[0]
	// match: (Cvt32Fto64F (Const32F [c]))
	// result: (Const64F [float64(c)])
	for {
		if v_0.Op != OpConst32F {
			break
		}
		c := auxIntToFloat32(v_0.AuxInt)
		v.reset(OpConst64F)
		v.AuxInt = float64ToAuxInt(float64(c))
		return true
	}
	return false
}
func rewriteValuegeneric_OpCvt32to32F(v *Value) bool {
	v_0 := v.Args[0]