lib.de.us

// VeryPositive SIA : http://very.lv
// Submitted by Danko Aleksejevs <******@****.***>
2038.io

// Vercel, Inc : https://vercel.com/
// Submitted by Connor Davis <security@vercel.com>
vercel.app
vercel.dev
now.sh

// Viprinet Europe GmbH : http://www.viprinet.com
// Submitted by Simon Kissel <******@****.***>
router.management

    kernel->out_size = TF_DataTypeSize(kernel->output_data_type);

    size_t check_size = std::max(kernel->in_size, kernel->out_size) %
                        std::min(kernel->in_size, kernel->out_size);
    if (check_size != 0) {
      std::ostringstream err;
      err << "cannot convert between datatype " << kernel->input_data_type
          << " and " << kernel->output_data_type;
      TF_SetStatus(s, TF_INVALID_ARGUMENT, err.str().c_str());
    }

	"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",

 protected:
  // Adds the kernel spec with the custom scale function for the kernel.
  LogicalResult RegisterKernel(llvm::StringRef kernel,
                               const KernelSpecs::Signature& signature,
                               const ScaleFn& fn, const ScaleDecomposeFn& dfn);

  // Adds the kernel spec with the scale constraint type for the kernel.
  LogicalResult RegisterKernel(llvm::StringRef kernel,

  return kernel;
}

void HistogramSummaryOp_Delete(void* kernel) {
  delete static_cast<HistogramSummaryOp*>(kernel);
}

template <typename T>
void HistogramSummaryOp_Compute(void* kernel, TF_OpKernelContext* ctx) {
  HistogramSummaryOp* k = static_cast<HistogramSummaryOp*>(kernel);
  TF_Tensor* tags;
  TF_Tensor* values;
  Safe_TF_StatusPtr status(TF_NewStatus());
  TF_GetInput(ctx, 0, &tags, status.get());

    ASSERT_EQ(1, list.kernel_size());
    ASSERT_EQ(device_name, list.kernel(0).device_type());
    TF_DeleteBuffer(buf);
    TF_DeleteStatus(status);
  }

  {
    Status status;
    std::unique_ptr<OpKernel> kernel =
        GetFakeKernel(device_name, op_name, node_name, &status);
    TF_EXPECT_OK(status);
    ASSERT_NE(nullptr, kernel.get());
    kernel->Compute(nullptr);
  }

  ASSERT_TRUE(delete_called);
}

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.

	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},

    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

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