// use_tuple_args: when this is true, always create a tuple argument for the
//   entry computation.
// enable_op_fallback: when this is true, prefer tf2xla fallback kernels over
// MLIR
//   native kernels for legalization to HLO.
// return_tuple: when this is true, always create a tuple result for the
//   entry computation.
// shape_determination_fns: Contains layout preference fn and shape

    // TODO(ycao): Support tensorlist-type output.
    out_desc.is_tensor_list = false;
  }

  // XLA computation always uses Tuple shape.
  *xla_output_shape = xla::ShapeUtil::MakeTupleShape(shapes);
  return absl::OkStatus();
}

// Creates a vector that maps from the parameters of the XLA computation to
// their original argument positions.
// MLIR-based TF-Compiler bridge doesn't have constant analysis yet, thus no

      old_parallel_execute, new_parallel_execute);

  // Extract inputs for each block of the parallel_execute op. The i-th
  // element in the list represents the input lists to TPU computation for
  // i-th logical core.
  llvm::SmallVector<llvm::SmallVector<mlir::Value, 4>, 4> input_list;
  builder->setInsertionPoint(*new_parallel_execute);
  auto result = tensorflow::ExtractInputsForLogicalDevices(

	})
	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)

#include "tensorflow/core/lib/strings/strcat.h"

namespace tensorflow {

/// @defgroup core Core Tensorflow API

class Output;

/// @addtogroup core
/// @{

/// Represents a node in the computation graph.
class Operation {
 public:
  Operation() : node_(nullptr) {}
  explicit Operation(Node* n);

  int32 num_inputs() const { return node_->num_inputs(); }

		}
	}

	return nil
}

// pkeEncrypt encrypt a plaintext message.
//
// It implements K-PKE.Encrypt according to FIPS 203 (DRAFT), Algorithm 13,
// although the computation of t and AT is done in parseEK.
func pkeEncrypt(cc *[CiphertextSize]byte, ex *encryptionKey, m *[messageSize]byte, rnd []byte) []byte {
	var N byte
	r, e1 := make([]nttElement, k), make([]ringElement, k)
	for i := range r {

  for (Region* region : regions_with_token) {
    // Initialize the token with the special argument token. This gets mapped to
    // input token in the parent op or a new token for the entry computation.
    auto token = StringAttr::get(ctx, tensorflow::kXlaTokenArgNodeName);
    for (Operation& op : region->getOps()) {
      // Only communication related ops that needs to have token should have the

  // outlined, as the constant operand is replaced by an argument.
  pm.addPass(mlir::createInlinerPass({}, NoCanonicalization));
  // Lift resource operations out of device computation. This step needs to be
  // done after inlining.
  pm.addPass(mlir::TFDevice::CreateResourceOpLiftingPass());
  // Outline clusters into cluster functions.
  pm.addPass(mlir::TFDevice::CreateClusterOutliningPass());

		if cfg.ExperimentErr != nil {
			base.Fatal(cfg.ExperimentErr)
		}
	}

	// Set environment (GOOS, GOARCH, etc) explicitly.
	// In theory all the commands we invoke should have
	// the same default computation of these as we do,
	// but in practice there might be skew
	// This makes sure we all agree.
	cfg.OrigEnv = toolchain.FilterEnv(os.Environ())
	cfg.CmdEnv = envcmd.MkEnv()
	for _, env := range cfg.CmdEnv {

// AssignVariableOps.
std::unique_ptr<OperationPass<ModuleOp>> CreateUnfreezeConstantsPass();

// Creates a pass that duplicates constants that affect the shape of a tensor
// after some computation.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateDuplicateShapeDeterminingConstantsPass();

// Creates a pass that creates a RestoreV2 op in the initializer function with