);

  let results = (outs);
}

def TF_DivOp : TF_Op<"Div", [Pure, ResultsBroadcastableShape, TF_SameOperandsAndResultElementTypeResolveRef]>,
               WithBroadcastableBinOpBuilder {
  let summary = "Returns x / y element-wise.";

  let description = [{
*NOTE*: `Div` supports broadcasting. More about broadcasting
[here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

}.timeline>div>.timeline-item>.timeline-header{border-bottom:1px solid rgba(0,0,0,.125);color:#495057;font-size:16px;line-height:1.1;margin:0;padding:10px}.timeline>div>.timeline-item>.timeline-header>a{font-weight:600}.timeline>div>.timeline-item>.timeline-body,.timeline>div>.timeline-item>.timeline-footer{padding:10px}.timeline>div>.timeline-item>.timeline-body>img{margin:10px}.timeline>div>.timeline-item>.timeline-body ol,.timeline>div>.timeline-item>.timeline-body ul,.timeline>div>.timeline-...

func rewriteValueARM64_OpARM64UDIV(v *Value) bool {
	v_1 := v.Args[1]
	v_0 := v.Args[0]
	// match: (UDIV x (MOVDconst [1]))
	// result: x
	for {
		x := v_0
		if v_1.Op != OpARM64MOVDconst || auxIntToInt64(v_1.AuxInt) != 1 {
			break
		}
		v.copyOf(x)
		return true
	}
	// match: (UDIV x (MOVDconst [c]))
	// cond: isPowerOfTwo64(c)
	// result: (SRLconst [log64(c)] x)
	for {

flex-start;\n}\n\n.media-body {\n  flex: 1;\n}\n","// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  display: flex;\n  flex-direction: column;\n\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 0;\n}\n\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive\n// list items. Includes an extra `.active` modifier class for selected...

and modulus operators (/, //, %) now match Python (flooring) semantics. This
applies to `tf.div` and `tf.mod` as well. To obtain forced integer truncation
based behaviors you can use `tf.truncatediv` and `tf.truncatemod`. *
`tf.divide()` is now the recommended division function. `tf.div()` will remain,
but its semantics do not respond to Python 3 or `from future` mechanisms. *