);

  TF_DerivedOperandTypeListAttr Tinputs = TF_DerivedOperandTypeListAttr<0>;
  TF_DerivedResultTypeListAttr Toutputs = TF_DerivedResultTypeListAttr<0>;
}

def TF_ModOp : TF_Op<"Mod", [Pure, ResultsBroadcastableShape, TF_SameOperandsAndResultElementTypeResolveRef]>,
               WithBroadcastableBinOpBuilder {
  let summary = [{

			return true
		}
		break
	}
	return false
}
func rewriteValueARM64_OpARM64MOD(v *Value) bool {
	v_1 := v.Args[1]
	v_0 := v.Args[0]
	// match: (MOD (MOVDconst [c]) (MOVDconst [d]))
	// cond: d != 0
	// result: (MOVDconst [c%d])
	for {
		if v_0.Op != OpARM64MOVDconst {
			break
		}
		c := auxIntToInt64(v_0.AuxInt)

* TensorFlow/models have been moved to a separate github repository. * Division
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,