})
	WorkloadServices.RegisterBatch(krt.BatchedEventFilter(
		func(a model.ServiceInfo) *workloadapi.Service {
			// Only trigger push if the XDS object changed; the rest is just for computation of others
			return a.Service
		},
		PushXds(a.XDSUpdater, func(i model.ServiceInfo) model.ConfigKey {
			return model.ConfigKey{Kind: kind.Address, Name: i.ResourceName()}
		})), false)

// after this.
//
// When TensorFlow needs to perform a computation with this kernel, it will
// call compute_func. This function will receive the pointer returned by
// create_func (or null if no create_func was provided), along with the inputs
// to the computation.
//
// The TF_OpKernelContext pointer received by compute_func is owned by

	NonMissingContainerRequests v1.ResourceList
}

// PodRequests computes the pod requests per the PodResourcesOptions supplied. If PodResourcesOptions is nil, then
// the requests are returned including pod overhead. The computation is part of the API and must be reviewed
// as an API change.
func PodRequests(pod *v1.Pod, opts PodResourcesOptions) v1.ResourceList {
	// attempt to reuse the maps if passed, or allocate otherwise

#include <algorithm>
#include <cinttypes>
#include <tuple>
#include <vector>

namespace mlir {
namespace TFL {

// A class that
// (1) Encodes in concise form the memory requirements of a computational graph
// (2) Allows for the fast simulation of changes to the peak memory requirement
//     under rematerialization of intermediate results in the graph
// (3) Implements a greedy algorithm for finding rematerializations of

        then:
        outputs.recompiledFiles("A", "ServiceRegistry", "ServiceRegistryResource.txt")
        serviceRegistryReferences("A", "B")
    }

    def "incremental processing works on subsequent incremental compilations after failure"() {
        def a = java "@Service class A {}"
        java "@Service class B {}"
        java "class Unrelated {}"

        outputs.snapshot { run "compileJava" }

        when:

  pm.addPass(mlir::TF::CreateTFShapeInferencePass());
  // For V1 compatibility, we process a module where the graph does not have
  // feeds and fetched. We extract first the TPU computation in a submodule,
  // where it'll be in a function with args and returned values, much more
  // like a TF v2 module. We can then run the usual pipeline on this nested

  for (auto& hardware : *hardwares) {
    if (hardware->hardware_typeid == type_id) {
      return &hardware->target_hardware_ops;
    }
  }
  return nullptr;
}

// A deny list for op cost computation since those ops are not arithemtic.
inline bool IsNonArithmeticOp(mlir::Operation* op) {
  if (llvm::isa<func::ReturnOp, func::FuncOp>(op)) return true;
  if (op->hasTrait<OpTrait::ConstantLike>()) return true;

  SavedModel saved_model;
  TF_RETURN_IF_ERROR(ReadBinaryProto(Env::Default(), pb_file, &saved_model));

  // Create a copy of `metagraph` which will be used and mutated for fingerprint
  // computation.
  FingerprintDef fingerprint_def;
  MetaGraphDef* metagraph = saved_model.mutable_meta_graphs(0);
  // Set fingerprint field #1.
  fingerprint_def.set_saved_model_checksum(HashSavedModel(saved_model));

// minimal set of changes to disable to provoke a failure.
//
// Finally, note that New returns a nil Matcher when there is no pattern,
// meaning that the target is not running under bisect at all.
// In that common case, the computation of the hash can be avoided entirely
// by checking for m == nil first:
//
//	for each change {
//		if m == nil {
//			enableChange()
//		} else {
//			h := bisect.Hash(file, line)
//			if m.ShouldEnable(h) {

         (str_attr.getValue().find("COMPOSITE") != llvm::StringRef::npos);
}
}  // namespace

// Rewrites unpartitioned resource reads and writes to partitioned resource
// reads and writes. The TPU computation from the frontend is generated in such
// a way that resource operations operate on the unpartitioned resource handle
// (from a `tf.TPUReplicatedInput`). This results in resource reads and writes