def ExpandTo4DForDepthwiseConv: NativeCodeCall<
  "ExpandTo4DForDepthwiseConv($0)">;

// If we see a (div or Mul) op (dividing/multiplying) a constant value
// to a convolution op with constant filter and bias, we can fuse the div/mul
// into the convolution op by constant folding
// the filter/bias and the div/mul op's constant operand.
// The following pattern restricts to float constant values for now.

// CHECK:  "tfl.zeros_like"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32>
}

func.func @div(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tf.Div"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0: tensor<1xf32>

// CHECK-LABEL: div
// CHECK:  tfl.div %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32>
// CHECK:  return
}

  func.return %0 : tensor<4x4x4x4xi32>
}

// CHECK-LABEL:   func @div(
// CHECK-SAME:              %[[VAL_0:.*]]: tensor<2xi32>) -> tensor<2xi32> {
// CHECK:           %[[VAL_1:.*]] = "tf.Div"(%[[VAL_0]], %[[VAL_0]]) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
// CHECK:           return %[[VAL_1]] : tensor<2xi32>
// CHECK:         }
func.func @div(%arg0: tensor<2xi32>) -> tensor<2xi32> {
  %0 = mhlo.divide %arg0, %arg0 : tensor<2xi32>

    border-radius: 1px;
}

.hamburger__bar + .hamburger__bar {
    margin-top: 4px;
}

.site-header {
    background-color: white;
}

/* Override javadoc styles */
.site-header div {
    font-family: 'Lato', Arial, sans-serif;
}

.site-header__navigation-header a {
    align-self: center;
    border-bottom: 0 none;
    height: 36px;
}

.site-header .site-header-version {

  );

  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)

	{ir.ODIV, types.TFLOAT32}: ssa.OpDiv32F,
	{ir.ODIV, types.TFLOAT64}: ssa.OpDiv64F,

	{ir.ODIV, types.TINT8}:   ssa.OpDiv8,
	{ir.ODIV, types.TUINT8}:  ssa.OpDiv8u,
	{ir.ODIV, types.TINT16}:  ssa.OpDiv16,
	{ir.ODIV, types.TUINT16}: ssa.OpDiv16u,
	{ir.ODIV, types.TINT32}:  ssa.OpDiv32,
	{ir.ODIV, types.TUINT32}: ssa.OpDiv32u,
	{ir.ODIV, types.TINT64}:  ssa.OpDiv64,

	FMULCC F1, F2, F3               // fc620073
	FMULS F1, F2                    // ec420072
	FMULS F1, F2, F3                // ec620072
	FMULSCC F1, F2, F3              // ec620073
	FDIV F1, F2                     // fc420824
	FDIV F1, F2, F3                 // fc620824
	FDIVCC F1, F2, F3               // fc620825
	FDIVS F1, F2                    // ec420824
	FDIVS F1, F2, F3                // ec620824

ngth);if(n||a){e.remove();return}e.value=r}parse(e){let t=zm(e),r=[],n=[];for(let a of t.nodes)n.push(a),a.type==="div"&&a.value===","&&(r.push(n),n=[]);return r.push(n),r.filter(a=>a.length>0)}stringify(e){if(e.length===0)return"";let t=[];for(let r of e)r[r.length-1].type!=="div"&&r.push(this.div(e)),t=t.concat(r);return t[0].type==="div"&&(t=t.slice(1)),t[t.length-1].type==="div"&&(t=t.slice(0,-2+1||void 0)),zm.stringify({nodes:t})}clone(e,t,r){let n=[],a=!1;for(let s of r)!a&&s.type==="word"...

		{name: "POPCNTB", argLength: 1, reg: gp11, asm: "POPCNTB"}, // number of set bits in each byte of arg0 placed in corresponding byte

		{name: "FDIV", argLength: 2, reg: fp21, asm: "FDIV"},   // arg0/arg1
		{name: "FDIVS", argLength: 2, reg: fp21, asm: "FDIVS"}, // arg0/arg1

		{name: "DIVD", argLength: 2, reg: gp21, asm: "DIVD", typ: "Int64"},   // arg0/arg1 (signed 64-bit)

        private val operatorWithAssignmentVariant = setOf(
            OperatorNameConventions.PLUS,
            OperatorNameConventions.MINUS,
            OperatorNameConventions.TIMES,
            OperatorNameConventions.DIV,
            OperatorNameConventions.REM,
            OperatorNameConventions.MOD,
        )

        private val callArgErrors = setOf(
            Errors.ARGUMENT_PASSED_TWICE,