mlir::LogicalResult PrepareParams();

  // Given the required_consts, it will fill the 3 output vectors with
  // their respective data.
  // Expressions: Output XLA expressions as required by the compiled kernel.
  // Tensors: Vector of tensors that back the TensorValue inputs
  // Inputs: Vector of inputs that are backed by tensors.
  mlir::LogicalResult PrepareKernelInputs(
      const llvm::SmallDenseSet<int>& required_consts,

calibration method and save the result to the given file path as a binary
proto file.)doc");

class CalibrationStatisticsSaverOp : public OpKernel {
 public:
  explicit CalibrationStatisticsSaverOp(
      absl::Nonnull<OpKernelConstruction*> context)
      : OpKernel(context) {
    std::string output_file_path;
    OP_REQUIRES_OK(context,
                   context->GetAttr("output_file_path", &output_file_path));

// e.g. `Add`, that expects its inputs in device memory. Here is how it
// works now.
// First, what do we mean by "op expects an input in XYZ memory"?
// There are two types of "ops" here: the tf2xla kernel and the HLO
// computation it builds. The tf2xla kernel needs to retrieve the actual
// numeric value of the compile-time constant tensors, so it really expects
// them to be on in host memory. However, for other inputs, it refers to them

}

def BatchFunctionOp : TensorflowMlrt_Op<"batch_function", [Pure]> {
  let summary = "Fallback ExecuteOp specialized for tf.BatchFunction.";

  let description = [{
    This kernel executes a variant tf.BatchFunction kernel that supports having
    the `f` attribute as a bytecode function.

    Example:
      %res = tf_mlrt.batch_function(%input, %captured_input)  {
          device = "/device:CPU:0",

	"k8s.io/apimachinery/pkg/util/version"
	"k8s.io/klog/v2"
	utilkernel "k8s.io/kubernetes/pkg/util/kernel"
)

type sysctl struct {
	// the name of sysctl
	name string
	// the minimum kernel version where the sysctl is available
	kernel string
}

var safeSysctls = []sysctl{
	{
		name: "kernel.shm_rmid_forced",
	}, {
		name: "net.ipv4.ip_local_port_range",
	}, {
		name: "net.ipv4.tcp_syncookies",

      continue;
    }

    devices->push_back(std::move(device));
  }
  return absl::OkStatus();
}

REGISTER_LOCAL_DEVICE_FACTORY(DEVICE_XLA_GPU, XlaGpuDeviceFactory);

// Kernel registrations

constexpr std::array<DataType, 20> kAllXlaGpuTypes = {
    {DT_UINT8,    DT_QUINT8,      DT_UINT16,        DT_INT8,       DT_QINT8,
     DT_INT16,    DT_INT32,       DT_QINT32,        DT_INT64,      DT_HALF,

#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/platform/test.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/status.h"

			return 0, false, nil
		case nil:
			if n == 0 {
				// If we did not read any bytes at all,
				// then this file may be in a file system
				// where copy_file_range silently fails.
				// https://lore.kernel.org/linux-fsdevel/******@****.***/T/#m05753578c7f7882f6e9ffe01f981bc223edef2b0
				if written == 0 {
					return 0, false, nil
				}
				// Otherwise src is at EOF, which means
				// we are done.

#include "absl/log/check.h"
#include "unsupported/Eigen/CXX11/Tensor"  // from @eigen_archive
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/resource_loader.h"
#include "tensorflow/core/runtime_fallback/kernel/kernel_fallback_op_handler.h"
#include "tensorflow/core/runtime_fallback/util/fallback_test_util.h"
#include "tensorflow/core/tfrt/utils/fallback_tensor.h"
#include "tfrt/bef/bef_buffer.h"  // from @tf_runtime

limitations under the License.
*/

package kernel

// IPLocalReservedPortsNamespacedKernelVersion is the kernel version in which net.ipv4.ip_local_reserved_ports was namespaced(netns).
// (ref: https://github.com/torvalds/linux/commit/122ff243f5f104194750ecbc76d5946dd1eec934)
const IPLocalReservedPortsNamespacedKernelVersion = "3.16"

// IPVSConnReuseModeMinSupportedKernelVersion is the minium kernel version supporting net.ipv4.vs.conn_reuse_mode.