// It does not include the time incurred in admission, or validation.
	StorageTracker DurationTracker

	// TransformTracker tracks the latency incurred in transforming the
	// response object(s) returned from the underlying storage layer.
	// This includes transforming the object to user's desired form
	// (ie. as Table), and also setting appropriate API level fields.
	// This does not include the latency incurred in serialization

			originalText := []byte(testText)

			transformedData, err := transformer.TransformToStorage(ctx, originalText, dataCtx)
			if err != nil {
				t.Fatalf("envelopeTransformer: error while transforming data to storage: %s", err)
			}
			untransformedData, _, err := transformer.TransformFromStorage(ctx, transformedData, dataCtx)
			if err != nil {

// defaultUpdatedObjectInfo implements UpdatedObjectInfo
type defaultUpdatedObjectInfo struct {
	// obj is the updated object
	obj runtime.Object

	// transformers is an optional list of transforming functions that modify or
	// replace obj using information from the context, old object, or other sources.
	transformers []TransformFunc
}

// form implemented by c.
func (c Caser) Bytes(b []byte) []byte {
	b, _, _ = transform.Bytes(c.t, b)
	return b
}

// String returns a string with the result of transforming s to the case form
// implemented by c.
func (c Caser) String(s string) string {
	s, _, _ = transform.String(c.t, s)
	return s
}

// Reset resets the Caser to be reused for new input after a previous call to

absl::StatusOr<std::optional<tensorflow::SaverDef>> CreateSaverDef(
    const std::vector<std::string>& control_ret_node_names,
    const tensorflow::GraphDef& graph_def);

// Adds passes for transforming the MLIR module op so that it can be exported
// back to GraphDef. Roughly, this consists of:
//   1) Inserting the @main function, which will become the main Graph.
//   2) Duplicating shape-determining constants.

      /*result=*/"success");

  return absl::OkStatus();
}

mlir::PassPipelineRegistration<> replicated_clustering_bridge_v2(
    "tf-replicated-clustering-bridge-v2",
    "Run all the passes involved in transforming a TensorFlow 2 graph before "
    "execution so that it is suitable for targeting devices.",
    CreateReplicatedClusteringPipelineV2);

}  // namespace v2
}  // namespace tf2xla

    - **Pilot** - Responsible for configuring the proxies at runtime.

    - **Citadel** - Responsible for certificate issuance and rotation.

    - **Galley** - Responsible for validating, ingesting, aggregating, transforming and distributing config within Istio.

- **Operator** - The component provides user friendly options to operate the Istio service mesh.

## Repositories

The Istio project is divided across a few GitHub repositories:

                @InputArtifact
                abstract Provider<FileSystemLocation> getInputArtifact()

                @Override
                void transform(TransformOutputs outputs) {
                    println "Transforming printed to System.out"
                    outputs.file(inputArtifact)
                }
            }
        """
        file('src/main/java/artifact/transform/sample/App.java') << """

	// Find all systemstack calls and record their targets. In
	// general, flow analysis can't see into systemstack, but it's
	// important to handle it for this check, so we model it
	// directly. This has to happen before transforming closures in walk since
	// it's a lot harder to work out the argument after.
	for _, n := range typecheck.Target.Funcs {
		c.curfn = n
		if c.curfn.ABIWrapper() {
			// We only want "real" calls to these

  // canonicalize/optimizer.
  static mlir::PassPipelineRegistration<StandardPipelineOptions> pipeline(
      "tf-standard-pipeline",
      "Run all the passes involved in transforming/optimizing the graph after "
      "importing into MLIR, without any target specialization.",
      CreateTFStandardPipeline);
}

}  // namespace TF