return false;
    }
    // FusedMatMul kernel does not support grad_a/grad_b attrs
    if ((matmul->hasAttr("grad_a") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) ||
        (matmul->hasAttr("grad_b") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {
        diag << "FusedMatMul kernel does not support grad_a/grad_b attrs";

	}
}

func newSecretStore(fakeClient clientset.Interface, clock clock.Clock, getTTL GetObjectTTLFunc, ttl time.Duration) *objectStore {
	return &objectStore{
		getObject:  getSecret(fakeClient),
		clock:      clock,
		items:      make(map[objectKey]*objectStoreItem),
		defaultTTL: ttl,
		getTTL:     getTTL,
	}
}

func getSecretNames(pod *v1.Pod) sets.Set[string] {
	result := sets.New[string]()

                       absl::StrCat("module_", module_name));
  auto parent_module = entry_func->getParentOfType<ModuleOp>();
  auto versions_attr = parent_module->getAttr(kVersionsAttr);
  if (!versions_attr)
    return parent_module.emitError(CreateMissingAttributeMsg(kVersionsAttr));

  module_for_func.get().getOperation()->setAttr(kVersionsAttr, versions_attr);

    auto range = llvm::make_filter_range(op->getUsers(), [&](Operation *user) {
      bool same_block = user->getBlock() == op->getBlock();
      bool same_device = op->getAttr(kDeviceAttr) == user->getAttr(kDeviceAttr);
      return same_block && same_device && state.IsMember(user);
    });
    return {range.begin(), range.end()};
  }

  // Users of block argument must be in the same block.

        exclude = set(exclude)
    if isinstance(obj, BaseModel):
        # TODO: remove when deprecating Pydantic v1
        encoders: Dict[Any, Any] = {}
        if not PYDANTIC_V2:
            encoders = getattr(obj.__config__, "json_encoders", {})  # type: ignore[attr-defined]
            if custom_encoder:
                encoders.update(custom_encoder)
        obj_dict = _model_dump(
            obj,
            mode="json",

  auto* defining_op = op->getOperand(operand_index).getDefiningOp();
  for (auto attr : kQuantizationAxisAttrs) {
    if (defining_op->hasAttr(attr)) {
      return defining_op->getAttr(attr);
    }
  }
  // Not found.
  return rewriter.getI64IntegerAttr(-1);
}

LogicalResult CheckIfAttrIs8Bit(const std::string& attr, Operation* op,
                                bool& is_8_bit) {

}

func (x *ForwardEchoRequest) GetMethod() string {
	if x != nil {
		return x.Method
	}
	return ""
}

func (x *ForwardEchoRequest) GetHttp2() bool {
	if x != nil {
		return x.Http2
	}
	return false
}

func (x *ForwardEchoRequest) GetHttp3() bool {
	if x != nil {
		return x.Http3
	}
	return false
}

func (x *ForwardEchoRequest) GetServerFirst() bool {
	if x != nil {

                                PatternRewriter& rewriter) const override {
    auto input_op = op.getInput().getDefiningOp();
    if (auto q = llvm::dyn_cast_or_null<QuantizeOp>(input_op)) {
      if (!q->getAttr(mlir::quant::kVolatileOpAttrName)) return failure();

      if (remove_volatile_ops_type == kPreserveInputsAndOutputs) {
        // Don't remove leading and trailing QDQ for PTQ workflow, so the io

        new_operands.push_back(build_list_op.getOut());
      } else {
        auto attr_name = compose_func.getArgAttrOfType<StringAttr>(
            arg.index(), kAttrArgumentNameAttr);
        auto attribute = op->getAttr(attr_name.getValue());
        if (!attribute) {
          attribute =
              compose_func.getArgAttr(arg.index(), kAttrArgumentDefaultAttr);
        }

  if (!op->hasAttr(TF::kTpuReplicateAttr)) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "missing _tpu_replicate attr");
    return false;
  }
  auto opattr = op->getAttr(TF::kTpuReplicateAttr);
  if (opattr != attr) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "invalid _tpu_replicate attr.")