hi := v_1.Args[0]
			v_1_1 := v_1.Args[1]
			if v_1_1.Op != OpAMD64NEGQ || bits != v_1_1.Args[0] {
				continue
			}
			v.reset(OpAMD64SHRDQ)
			v.AddArg3(lo, hi, bits)
			return true
		}
		break
	}
	// match: (ORQ (SHLQ lo bits) (SHRQ hi (NEGQ bits)))
	// result: (SHLDQ lo hi bits)
	for {
		for _i0 := 0; _i0 <= 1; _i0, v_0, v_1 = _i0+1, v_1, v_0 {
			if v_0.Op != OpAMD64SHLQ {

            "description": "Optional: mode bits used to set permissions on this file, must be an octal value between 0000 and 0777 or a decimal value between 0 and 511. YAML accepts both octal and decimal values, JSON requires decimal values for mode bits. If not specified, the volume defaultMode will be used. This might be in conflict with other options that affect the file mode, like fsGroup, and the result can be other mode bits set.",
            "format": "int32",

                      WithBroadcastableBinOpBuilder {
  let summary = "Elementwise computes the bitwise AND of `x` and `y`.";

  let description = [{
The result will have those bits set, that are set in both `x` and `y`. The
computation is performed on the underlying representations of `x` and `y`.

For example:

```python
import tensorflow as tf

pkg hash/maphash, type Seed struct
pkg log, const Lmsgprefix = 64
pkg log, const Lmsgprefix ideal-int
pkg math, func FMA(float64, float64, float64) float64
pkg math/bits, func Rem(uint, uint, uint) uint
pkg math/bits, func Rem32(uint32, uint32, uint32) uint32
pkg math/bits, func Rem64(uint64, uint64, uint64) uint64
pkg mime/multipart, method (*Reader) NextRawPart() (*Part, error)
pkg net/http, method (Header) Values(string) []string

		{"(*Int).And", Method, 0},
		{"(*Int).AndNot", Method, 0},
		{"(*Int).Append", Method, 6},
		{"(*Int).Binomial", Method, 0},
		{"(*Int).Bit", Method, 0},
		{"(*Int).BitLen", Method, 0},
		{"(*Int).Bits", Method, 0},
		{"(*Int).Bytes", Method, 0},
		{"(*Int).Cmp", Method, 0},
		{"(*Int).CmpAbs", Method, 10},
		{"(*Int).Div", Method, 0},
		{"(*Int).DivMod", Method, 0},
		{"(*Int).Exp", Method, 0},

        [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/).
        Specifically, inputs to such ops are rounded from 23 bits of precision
        to 10 bits of precision. This is unlikely to cause issues in practice
        for deep learning models. In some cases, TensorFloat-32 is also used for
        complex64 ops. TensorFloat-32 can be disabled by running

pkg math (freebsd-riscv64-cgo), const MaxUint = 18446744073709551615 #53466
pkg math (freebsd-riscv64-cgo), const MinInt = -9223372036854775808 #53466
pkg math/bits (freebsd-riscv64), const UintSize = 64 #53466
pkg math/bits (freebsd-riscv64-cgo), const UintSize = 64 #53466
pkg os (freebsd-riscv64), const DevNull = "/dev/null" #53466
pkg os (freebsd-riscv64), const O_APPEND = 8 #53466