op_VESLV  uint32 = 0xE770 // 	VRR-c	VECTOR ELEMENT SHIFT LEFT
	op_VESL   uint32 = 0xE730 // 	VRS-a	VECTOR ELEMENT SHIFT LEFT
	op_VESRA  uint32 = 0xE73A // 	VRS-a	VECTOR ELEMENT SHIFT RIGHT ARITHMETIC
	op_VESRAV uint32 = 0xE77A // 	VRR-c	VECTOR ELEMENT SHIFT RIGHT ARITHMETIC
	op_VESRL  uint32 = 0xE738 // 	VRS-a	VECTOR ELEMENT SHIFT RIGHT LOGICAL
	op_VESRLV uint32 = 0xE778 // 	VRR-c	VECTOR ELEMENT SHIFT RIGHT LOGICAL

    const auto [input_height, input_width, output_height, output_width] =
        GetInOutDimensions(op, dimension_numbers);
    const auto [stride_height, stride_width] = GetStrides(op);

    // Below convolution arithmetic for `SAME` padding calculation is
    // referenced from
    // https://www.tensorflow.org/api_docs/python/tf/nn/convolution. The
    // following condition must hold true for padding to be `SAME`:

		fpstoreidx = regInfo{inputs: []regMask{gpspsb, gpsp, fp, 0}}

		prefreg = regInfo{inputs: []regMask{gpspsbg}}
	)

	var AMD64ops = []opData{
		// {ADD,SUB,MUL,DIV}Sx: floating-point arithmetic
		// x==S for float32, x==D for float64
		// computes arg0 OP arg1
		{name: "ADDSS", argLength: 2, reg: fp21, asm: "ADDSS", commutative: true, resultInArg0: true},

x <= f()
^a >> b
f() || g()
x == y+1 && <-chanInt > 0
</pre>


<h3 id="Arithmetic_operators">Arithmetic operators</h3>
<p>
Arithmetic operators apply to numeric values and yield a result of the same
type as the first operand. The four standard arithmetic operators (<code>+</code>,
<code>-</code>, <code>*</code>, <code>/</code>) apply to integer,

// CHECK: return %arg0, %arg1
}

// CHECK-LABEL: testSubOfZeroWithBroadcasting
func.func @testSubOfZeroWithBroadcasting(%arg0: tensor<4x1xf32>) -> tensor<4x4xf32> {
  // This is an identity arithmetic operation, however we do not currently fold
  // it because it has a broadcasting.
  %0 = "tf.Const"() {value = dense<[[0.0, 0.0, 0.0, 0.0]]> : tensor<1x4xf32>} : () -> tensor<1x4xf32>

      mac_str = absl::StrFormat("%.3f G ",
                                static_cast<double>(ops_count / 2) / billion);
    }
    LOG(INFO) << "Estimated count of arithmetic ops: " << flops_str
              << " ops, equivalently " << mac_str << " MACs";
  }

  std::string model_description;
  if (auto attr = module_->getAttrOfType<StringAttr>("tfl.description")) {

// This implements the shell quoting semantics described in
// https://pubs.opengroup.org/onlinepubs/9699919799/utilities/V3_chap02.html#tag_18_02,
// except that it does not support parameter or arithmetic expansion or command
// substitution and hard-codes the <blank> delimiters instead of reading them
// from LC_LOCALE.
func splitPkgConfigOutput(out []byte) ([]string, error) {
	if len(out) == 0 {
		return nil, nil

// TODO: we should be able to get rid of MOVDnop all together.
// But for now, this is enough to get rid of lots of them.
(MOVDnop (MOVDconst [c])) => (MOVDconst [c])

// fold constant into arithmetic ops
(ADD  x (MOVDconst <t> [c])) && !t.IsPtr() => (ADDconst [c] x)
(SUB  x (MOVDconst [c])) => (SUBconst [c] x)
(AND  x (MOVDconst [c])) => (ANDconst [c] x)
(OR   x (MOVDconst [c])) => (ORconst  [c] x)

(If (ConstBool [c]) yes no) && !c => (First no yes)

(Phi <t> nx:(Not x) ny:(Not y)) && nx.Uses == 1 && ny.Uses == 1 => (Not (Phi <t> x y))

// Get rid of Convert ops for pointer arithmetic on unsafe.Pointer.
(Convert (Add(64|32) (Convert ptr mem) off) mem) => (AddPtr ptr off)
(Convert (Convert ptr mem) mem) => ptr

// strength reduction of divide by a constant.

    QuantizableResult,
    Pure]> {
  let summary = "Broadcast an array for a compatible shape.";

  let description = [{
Broadcasting is the process of making arrays to have compatible shapes
for arithmetic operations. Two shapes are compatible if for each
dimension pair they are either equal or one of them is one. When trying
to broadcast a Tensor to a shape, it starts with the trailing dimensions,
and works its way forward.