#include "tensorflow/c/experimental/ops/gen/common/case_format.h"

#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace generator {

namespace {

// For each test case, we manually construct the 4 variations in string case and
// test all 16 conversions: from and to each of the 4 string case variations.
struct Variations {

	listConfigMap := func(namespace string, opts metav1.ListOptions) (runtime.Object, error) {
		return kubeClient.CoreV1().ConfigMaps(namespace).List(context.TODO(), opts)
	}
	watchConfigMap := func(namespace string, opts metav1.ListOptions) (watch.Interface, error) {
		return kubeClient.CoreV1().ConfigMaps(namespace).Watch(context.TODO(), opts)
	}
	newConfigMap := func() runtime.Object {

	listSecret := func(namespace string, opts metav1.ListOptions) (runtime.Object, error) {
		return kubeClient.CoreV1().Secrets(namespace).List(context.TODO(), opts)
	}
	watchSecret := func(namespace string, opts metav1.ListOptions) (watch.Interface, error) {
		return kubeClient.CoreV1().Secrets(namespace).Watch(context.TODO(), opts)
	}
	newSecret := func() runtime.Object {

#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"

namespace mlir {
namespace lite {

// Sparsify the `input_model` and write the result to a flatbuffer `builder`.
absl::Status SparsifyModel(const tflite::ModelT& input_model,
                           flatbuffers::FlatBufferBuilder* builder);
}  // namespace lite
}  // namespace mlir

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project

namespace mlir {

namespace func {
class FuncOp;
}
class ModuleOp;
class Operation;
template <typename T>
class OperationPass;
class Pass;

namespace mhlo {

/// Converter to be used along with the fallback Tf2Xla patterns below.
class Tf2XlaTypeConverter : public TypeConverter {

// examples implements ExampleInterface
type examples struct {
	client rest.Interface
	ns     string
}

// newExamples returns a Examples
func newExamples(c *CrV1Client, namespace string) *examples {
	return &examples{
		client: c.RESTClient(),
		ns:     namespace,
	}
}

// Get takes name of the example, and returns the corresponding example object, and an error if there is any.

#include "tensorflow/core/util/debug_data_dumper.h"
#include "tsl/lib/monitoring/counter.h"
#include "tsl/platform/error_logging.h"
#include "tsl/platform/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v2 {

using mlir::LogicalResult;
using mlir::ModuleOp;
using mlir::OpPassManager;
using mlir::PassManager;
using mlir::func::FuncOp;

#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/graph/graph.h"

namespace tensorflow {
namespace tf2xla {
namespace v2 {

// Given an MLIR module, returns a GraphDef.
absl::StatusOr<std::unique_ptr<GraphDef>> ConvertMlirToGraphdef(
    mlir::ModuleOp module, const GraphExportConfig& configs);

//
// Instead, we rely directly on the procfs.
// This rules out two possible methods:
// * use crictl to inspect the pod; this returns the bind-mounted network namespace file.
// * /var/lib/cni/results shows the outputs of CNI plugins; this containers the bind-mounted network namespace file.
//
// Instead, we traverse the procfs. Comments on this method are inline.
func getPodNetNs(pod *corev1.Pod) (string, error) {

#include "tensorflow/c/experimental/ops/gen/common/view_util.h"

#include "absl/strings/str_join.h"
#include "absl/strings/substitute.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace generator {

string Call(const string& object, const string& method,
            std::vector<string> arguments, const char* oper) {
  return absl::Substitute("$0$1$2($3)", object, oper, method,