kernel = tf.transpose(kernel, [0, 2, 1, 3, 4, 5])
    kernel = tf.reshape(kernel, [4, 4, int(channel), filters])
    kernel = tf.cast(kernel, inputs.dtype)
    ```

    If kernel\_size % block\_size != 0, padding is needed for the weight before
    transform, input of Convolution needs to be padded as well.

1.  Rewrite the first convolution

    "//tensorflow/core/kernels:variable_ops",
    "//tensorflow/core/kernels/data:finalize_dataset_op",
    "//tensorflow/core/kernels/data:generator_dataset_op",
    "//tensorflow/core/kernels/data:iterator_ops",
    "//tensorflow/core/kernels/data:optional_ops",
    "//tensorflow/core/kernels/data:prefetch_dataset_op",
    "//tensorflow/core/kernels/data:options_dataset_op",
    "//tensorflow/core/profiler/lib:traceme",

	type Test struct {
		sysctls []string
		err     bool
	}
	for _, test := range []Test{
		{sysctls: []string{"kernel.msg*", "kernel.sem"}},
		{sysctls: []string{"kernel/msg*", "kernel/sem"}},
		{sysctls: []string{" kernel.msg*"}, err: true},
		{sysctls: []string{"kernel.msg* "}, err: true},
		{sysctls: []string{"net.-"}, err: true},
		{sysctls: []string{"net.*.foo"}, err: true},

  attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "dense_2/Add:0"}, tf_saved_model.exported_names = ["serving_default"]} {

#ifndef TENSORFLOW_C_KERNELS_EXPERIMENTAL_H_
#define TENSORFLOW_C_KERNELS_EXPERIMENTAL_H_

#include "tensorflow/c/c_api_macros.h"
#include "tensorflow/c/kernels.h"

// --------------------------------------------------------------------------
// Experimental kernel C API for TensorFlow.
//
// The API here is subject to changes in the future.
// --------------------------------------------------------------------------

```
kernel/net/ipv4/netfilter/nf_conntrack_ipv4.ko
kernel/net/netfilter/ipvs/ip_vs.ko
kernel/net/netfilter/ipvs/ip_vs_rr.ko
kernel/net/netfilter/ipvs/ip_vs_wrr.ko
kernel/net/netfilter/ipvs/ip_vs_lc.ko
kernel/net/netfilter/ipvs/ip_vs_wlc.ko
kernel/net/netfilter/ipvs/ip_vs_fo.ko
kernel/net/netfilter/ipvs/ip_vs_ovf.ko
kernel/net/netfilter/ipvs/ip_vs_lblc.ko
kernel/net/netfilter/ipvs/ip_vs_lblcr.ko

// derived from other ports by the target specification of the kernel.
def Quantization_QuantizeRegionOp : Quantization_Op<"region", [
    Pure,
    IsolatedFromAbove,
    SingleBlockImplicitTerminator<"ReturnOp">]> {
  let summary = [{
    The `region` operation wraps high-precision ops as a logical low-precision
    quantized kernel.
  }];

  let arguments = (ins Variadic<AnyType>:$inputs,

  attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "dense_2/Add:0"}, tf_saved_model.exported_names = ["serving_default"]} {

        dim {
          size: 64
        }
      }
    }
  }
  attr {
    key: "shared_name"
    value {
      s: "training/SGD/conv1/kernel/momentum"
    }
  }
}
node {
  name: "training/SGD/gradients/loss_1/conv1/kernel/Regularizer/Square_grad/Mul"
  op: "Const"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "dtype"
    value {

See the License for the specific language governing permissions and
limitations under the License.
*/

package kernel

import (
	"fmt"
	"os"
	"strings"

	"k8s.io/apimachinery/pkg/util/version"
)

type readFileFunc func(string) ([]byte, error)

// GetVersion returns currently running kernel version.
func GetVersion() (*version.Version, error) {
	return getVersion(os.ReadFile)
}