# Experimental extensions to the C API for eager execution of kernels.

load(
    "//tensorflow:tensorflow.bzl",
    "tf_cc_test",
    "tf_copts",
    "tf_cuda_cc_test",
    "tf_cuda_library",
)
load("//tensorflow:tensorflow.default.bzl", "cc_header_only_library", "filegroup")
load(
    "//tensorflow/core/platform:build_config_root.bzl",
    "tf_cuda_tests_tags",
)
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

// and return "non-ready" handles in `retvals`. Note that any handles contained
// in 'op' should not be mutated till the kernel execution actually finishes.
//
// For sync execution, if any of the inputs to `op` are not ready, this call
// will block till they become ready and then return when the kernel execution
// is done.
// TODO(agarwal): change num_retvals to int from int*.

// function. Otherwise return nullptr in result and error Status from the
// function.
//
// If `library_filename` has already been loaded, we return a cached handle.
// Device and Kernels/Ops are registered as globals when a library is loaded
// for the first time.
TF_Library* TF_LoadPluggableDeviceLibrary(const char* library_filename,
                                          TF_Status* status) {

  config_info_line('monolithic', 'Config for mostly static monolithic build.')
  config_info_line('numa', 'Build with NUMA support.')
  config_info_line(
      'dynamic_kernels',
      '(Experimental) Build kernels into separate shared objects.')
  config_info_line('v1', 'Build with TensorFlow 1 API instead of TF 2 API.')

  print('Preconfigured Bazel build configs to DISABLE default on features:')

#include <time.h>
#include <unistd.h>

#ifdef _WIN32
#include <process.h>
#endif

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/kernels.h"

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();

### Bug or Regression

- Bump node-problem-detector version to v0.8.5 to fix OOM detection in with Linux kernels 5.1+ ([#96716](https://github.com/kubernetes/kubernetes/pull/96716), [@tosi3k](https://github.com/tosi3k)) [SIG Cloud Provider, Scalability and Testing]

  KernelList kernel_list;
  kernel_list.ParseFromArray(kernel_list_buf->data, kernel_list_buf->length);
  ASSERT_EQ(kernel_list.kernel_size(), 1);
  EXPECT_EQ(kernel_list.kernel(0).op(), "TestOpWithSingleKernel");
  EXPECT_EQ(kernel_list.kernel(0).device_type(), "CPU");
  TF_DeleteBuffer(kernel_list_buf);
  TF_DeleteStatus(status);
}

TEST(TestKernel, TestGetRegisteredKernelsForOpNoKernels) {

### Bug or Regression

- Removed a warning related to Linux user namespaces and kernel version. Previously, if the feature gate `UserNamespacesSupport` was enabled, the kubelet warned when detecting a Linux kernel version earlier than 6.3.0. While user namespace support generally requires kernel 6.3 or newer, it can also work on older kernels. ([#130243](https://github.com/kubernetes/kubernetes/pull/130243), [@rata](https://github.com/rata))