mlir::OwningOpRef<mlir::ModuleOp> module_ref =
      mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));

  AddDevicesToOp(*module_ref, /*device_set=*/nullptr);
  EXPECT_EQ((*module_ref)->getAttr("tf.devices"), nullptr);
}

TEST(DeviceUtilTest, GetDevicesFromOpNoDevicesAttribute) {
  mlir::MLIRContext context;
  mlir::OwningOpRef<mlir::ModuleOp> module_ref =

                parameters.getOptions().add(docTitle);
            }

            // None of these options work for Scala >=2.8
            // options.getBottom();;
            // options.getTop();
            // options.getHeader();
            // options.getWindowTitle();

            List<String> additionalParameters = options.getAdditionalParameters();
            if (additionalParameters != null) {

        this.jdt = jdt;
    }

    /**
     * Configures eclipse wtp information
     * <p>
     * For examples see docs for {@link EclipseWtp}
     */
    public EclipseWtp getWtp() {
        if (wtp == null) {
            wtp = getObjectFactory().newInstance(EclipseWtp.class);
        }
        return wtp;
    }

    public void setWtp(EclipseWtp wtp) {
        this.wtp = wtp;

mlir::Attribute GetDeviceOfResource(mlir::func::FuncOp func,
                                    mlir::Value resource) {
  if (auto* resource_op = resource.getDefiningOp()) {
    return resource_op->getAttr(kDeviceAttr);
  } else {
    const auto resource_arg = resource.dyn_cast_or_null<BlockArgument>();
    if (resource_arg && (resource_arg.getOwner() == &(func.front()))) {
      return func.getArgAttrOfType<mlir::StringAttr>(

        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

    return failure();
  }

  // Collect all the undefined attributes used in the inputs.
  llvm::SmallVector<StringAttr, 4> undefined_attrs;
  for (auto attr : input_used_attrs) {
    if (!func->getAttr(attr.getValue())) {
      undefined_attrs.push_back(attr);
    }
  }

  // Verify the argument order: tensors, tensor list, attributes; and also
  // verify there is at most one tensor list argument.

        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);
        }