func NewCachingSecretManager(kubeClient clientset.Interface, getTTL manager.GetObjectTTLFunc) Manager {
	getSecret := func(namespace, name string, opts metav1.GetOptions) (runtime.Object, error) {
		return kubeClient.CoreV1().Secrets(namespace).Get(context.TODO(), name, opts)
	}
	secretStore := manager.NewObjectStore(getSecret, clock.RealClock{}, getTTL, defaultTTL)
	return &secretManager{

  void runOnOperation() override {
    auto module = getOperation();
    // If TFLite variable legalization is not allowed, then we skip this pass.
    if (auto legalize_tfl_variables_attr =
            module->getAttr(kLegalizeTflVariables)) {
      if (!mlir::cast<BoolAttr>(legalize_tfl_variables_attr).getValue()) return;
    }

    RewritePatternSet patterns(&getContext());
    populateWithGenerated(patterns);

             << type_string() << " on an XLA device. This should never happen.";
}

XlaAssignVariableOp::XlaAssignVariableOp(OpKernelConstruction* c)
    : OpKernel(c) {
  OP_REQUIRES_OK(c, c->GetAttr("dtype", &dtype_));
}

void XlaAssignVariableOp::Compute(OpKernelContext* context) {
  OP_REQUIRES(context, dtype_ == context->input(1).dtype(),
              errors::InvalidArgument(

    OpBuilder builder(op.getContext());
    SmallVector<Type> result_types(op.getResultTypes().begin(),
                                   op.getResultTypes().end());
    if (!op->getAttr("layout")) {
      auto layout = mlir::GetTPUInfeedLayout(result_types, builder);
      if (failed(layout)) return;

      op->setAttr("layout", layout.value());
    }
  }
};

    }
  }

  if (!in_order) {
    // Functions do not get reused in practice, so skip the check for if the
    // callee has been updated.
    StringAttr callee_sym = cluster_func_op.getFuncAttr().getAttr();
    MoveResourceArgsToEnd(symtab.lookup<func::FuncOp>(callee_sym));
  }
  builder.setInsertionPoint(cluster_func_op);
  auto xla_launch_op = builder.create<TF::XlaLaunchOp>(

  auto walk_result = func_op->walk([&](mlir::Operation* op) {
    // Convert `_tpu_replicate`.
    if (op->hasAttr(TF::kTpuReplicateAttr)) {
      op->setAttr(tensorflow::kReplicationInfoAttr,
                  op->getAttr(TF::kTpuReplicateAttr));
      op->removeAttr(tensorflow::kTpuReplicateAttr);
      op->setAttr(tensorflow::kCompileDeviceTypeAttr,
                  builder.getStringAttr(tensorflow::kTpuDevice));
    }

 */
package org.gradle.api.internal.file.pattern;

public class PatternStepFactory {
    private static final AnyWildcardPatternStep ANY_WILDCARD_PATTERN_STEP = new AnyWildcardPatternStep();

    public static PatternStep getStep(String source, boolean caseSensitive) {
        if (source.length() == 0) {
            return new FixedPatternStep(source, caseSensitive);
        }

  StringAttr device = mlir::cast<StringAttr>(
      added_func_op->getRegion(0).getBlocks().front().front().getAttr(kDevice));
  StringAttr inference_type = mlir::cast<StringAttr>(
      added_func_op->getRegion(0).getBlocks().front().front().getAttr(
          kInferenceType));
  added_call_op->setAttr(kDevice, device);
  added_call_op->setAttr(kInferenceType, inference_type);

	a.Atr = attr
	a.Cls = uint8(cls)
	a.Value = value
	a.Data = data
}

// Each DIE (except the root ones) has at least 1 attribute: its
// name. getattr moves the desired one to the front so
// frequently searched ones are found faster.
func getattr(die *dwarf.DWDie, attr uint16) *dwarf.DWAttr {
	if die.Attr.Atr == attr {
		return die.Attr
	}

	a := die.Attr
	b := a.Link
	for b != nil {

  LogicalResult matchAndRewrite(mlir::stablehlo::CustomCallOp op,
                                PatternRewriter &rewriter) const override {
    auto backendConfig =
        mlir::dyn_cast<DictionaryAttr>(op->getAttr("composite.backend_config"));
    if (!backendConfig)
      return op->emitError(
          "custom_call has no 'composite.backend_config' attribute or the "
          "attribute is not a dictionary");