Scope root = Scope::NewRootScope()
                   .ExitOnError()
                   .WithAssignedDevice(kDeviceName)
                   .WithXlaCluster("cluster_0");

  Output input_float =
      ops::Placeholder(root.WithOpName("input_float"), DT_FLOAT);
  Output input_i64 = ops::Placeholder(root.WithOpName("input_i64"), DT_INT64);

  Output begin_begin =
      ops::Placeholder(root.WithOpName("begin_begin"), DT_INT32);

    // Calculate min/max statistics.
    const auto input_flat = input_tensor.flat<float>();
    Tensor *min_output = nullptr, *max_output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("min", {}, &min_output));
    OP_REQUIRES_OK(context, context->allocate_output("max", {}, &max_output));
    min_output->scalar<float>().device(
        context->template eigen_device<CPUDevice>()) = input_flat.minimum();
    max_output->scalar<float>().device(

        long value = random.nextLong();
        for (PrimitiveSink sink : sinks) {
          sink.putLong(value);
        }
      }
    },
    PUT_FLOAT() {
      @Override
      void performAction(Random random, Iterable<? extends PrimitiveSink> sinks) {
        float value = random.nextFloat();
        for (PrimitiveSink sink : sinks) {
          sink.putFloat(value);