.Attr("Tresults: list(type) >= 0")
    .Attr("function: func")
    // XLA random-number generation ops are stateful.
    // TODO(phawkins): create stateful and non-stateful variants of XlaLaunch.
    .SetIsStateful()
    .Doc("XLA Launch Op. For use by the XLA JIT only.");

REGISTER_OP("XlaLaunchV2")
    .Input("args: Targs")
    .Output("results: Tresults")
    .Attr("Targs: list(type) >= 0")

)doc")
    .Attr("container: string = ''")
    .Attr("shared_name: string = ''")
    .Attr("debug_name: string = ''")
    .Attr("dtype: type")
    .Attr("shape: shape")
    .Output("resource: resource")
    .SetIsStateful()
    .SetShapeFn([](shape_inference::InferenceContext* c) {
      c->set_output(0, c->Scalar());
      DataType t;
      TF_RETURN_IF_ERROR(c->GetAttr("dtype", &t));
      PartialTensorShape p;

    .Input("tensor_data: T")
    .Attr("log_dir_path: string")
    .Attr("file_name: string")
    .Attr("T: type")
    .Attr("enabled: bool")
    .Attr("func_name: string")
    .Attr("node_name: string")
    .SetIsStateful();

class DumpTensorOp : public OpKernel {
 public:
  explicit DumpTensorOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
    string log_dir_path;
    string file_name;
    string func_name;

REGISTER_OP("CalibrationStatisticsSaver")
    .Input("args: Tin")
    .Attr("Tin: list(type) >= 0")
    .Attr("ids: list(string) >= 1")
    .Attr("calibration_methods: list(int) >= 1")
    .Attr("output_file_path: string")
    .SetIsStateful()
    .Doc(R"doc(
Aggregates and saves the calibration statistics data.

This op collects outputs of multiples CustomAggregator ops, which includes
`min`, `max` and `histogram`. Then it aggregates them according to the