mlir::Value y) -> mlir::Operation* {
             return opb.create<mlir::TF::AddV2Op>(loc, x, y).getOperation();
           });

  py::class_<mlir::TF::AnyOp>(m, "Tf_AnyOp")
      .def("create",
           [](mlir::OpBuilder& opb, mlir::Location loc, mlir::Value input,
              mlir::Value reduction_indices,
              bool keep_dims = false) -> mlir::Operation* {

class Tf_AddV2Op:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value) -> Operation: ...

class Tf_AnyOp:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value, arg3: bool) -> Operation: ...

class Tf_ConstOp:
    def __init__(self, *args, **kwargs) -> None: ...

def LegalizeProd : Pat<
  (TF_ProdOp $arg0, $axes, BoolAttr:$arg2),
  (TFL_ReduceProdOp $arg0, (CreateTFCastToInt32Op $axes), $arg2)>;

def LegalizeAny : Pat<
  (TF_AnyOp $input, $reduction_indices, $keep_dims),
  (TFL_ReduceAnyOp $input, (CreateTFCastToInt32Op $reduction_indices),
                   $keep_dims)>;

def LegalizeAll : Pat<

    TF_BoolTensor:$reshuffle
  );

  let results = (outs
    Res<TF_ResourceTensor, "", [TF_DatasetSeedGeneratorAlloc]>:$handle,
    TF_VariantTensor:$deleter
  );
}

def TF_AnyOp : TF_Op<"Any", [Pure]> {
  let summary = [{
Computes the "logical or" of elements across dimensions of a tensor.
  }];

  let description = [{
Reduces `input` along the dimensions given in `axis`. Unless