LogicalResult matchAndRewrite(TF::LookupTableFindV2Op find_op,
                                PatternRewriter& rewriter) const override {
    auto handle_op = find_op.getTableHandle().getDefiningOp();
    if (handle_op == nullptr) return failure();
    auto hashtable_op = llvm::dyn_cast<TFL::HashtableOp>(handle_op);
    if (hashtable_op == nullptr) return failure();
    rewriter.replaceOpWithNewOp<TFL::HashtableFindOp>(

  func.return %2 : tensor<i32>
}

func.func @tensor_array_while_cond(%index: tensor<i32>, %size: tensor<i32>, %flow_0: tensor<f32>, %flow_1: tensor<f32>, %handle_0: tensor<2x!tf_type.resource<tensor<?x100xf32>>>, %handle_1: tensor<2x!tf_type.resource<tensor<?x512xf32>>>) -> (tensor<i1>) {
  %0 = "tf.Less"(%index, %size) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

    %handle_0 = "tf.VarHandleOp"() {container="c", shared_name="a"} : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
    %handle_1 = "tf.VarHandleOp"() {container="c", shared_name="b"} : () -> tensor<!tf_type.resource<tensor<1x10xi64>>>
    %0 = "tf.ReadVariableOp"(%handle_0) : (tensor<!tf_type.resource<tensor<1x10xf32>>>) -> tensor<1x10xf32>

  // CHECK: [[handle_0:%.*]] = mlrt.async([[input0]], [[promises]]#0)
  // CHECK-SAME: callee = @main_stream_0
  %handle_0 = mlrt.async(%input0, %promise_b)
    {callee = @main_stream_0} :
    (tensor<i32>, !mlrt.promise) -> !mlrt.async_handle
  // CHECK: [[handle_1:%.*]] = mlrt.async([[input1]], [[promises]]#1, [[promises]]#2)
  // CHECK-SAME: callee = @main_stream_1
  %handle_1 = mlrt.async(%input1, %promise_c, %promise_d)

  // CHECK: "tf.TensorArrayV3"
  %handle_12, %flow_13 = "tf.TensorArrayV3"(%max_iter) {device = "/job:localhost/replica:0/task:0/device:CPU:0", dtype = !tf_type.variant, dynamic_size = false, element_shape = #tf_type.shape<*>, identical_element_shapes = true, tensor_array_name = ""} : (tensor<i32>) -> (tensor<2x!tf_type.resource<tensor<*x!tf_type.variant>>>, tensor<f32>)
  // CHECK: "tf.TensorArrayV3"

}

TapeTensor::TapeTensor(AbstractTensorHandle* handle) : handle_(handle) {
  handle_->Ref();
}
TapeTensor::TapeTensor(const TapeTensor& other) {
  handle_ = other.handle_;
  handle_->Ref();
}
TapeTensor::~TapeTensor() { handle_->Unref(); }

int64_t TapeTensor::GetID() const { return ToId(handle_); }

tensorflow::DataType TapeTensor::GetDType() const {
  return handle_->DataType();
}

	Arbitrary map[string]any `json:"-"`
}

func NewUDSLogger(level istiolog.Level) *UDSLogger {
	l := &UDSLogger{}
	mux := http.NewServeMux()
	mux.HandleFunc(constants.UDSLogPath, l.handleLog)
	loggingServer := &http.Server{
		Handler: mux,
	}
	l.loggingServer = loggingServer
	pluginLog.SetOutputLevel(level)
	return l
}

import org.gradle.launcher.daemon.server.api.HandleInvalidateVirtualFileSystem;
import org.gradle.launcher.daemon.server.api.HandleReportStatus;
import org.gradle.launcher.daemon.server.api.HandleStop;
import org.gradle.launcher.daemon.server.exec.CleanUpVirtualFileSystemAfterBuild;
import org.gradle.launcher.daemon.server.exec.DaemonCommandExecuter;
import org.gradle.launcher.daemon.server.exec.EstablishBuildEnvironment;

  while_op.erase();
  return success();
}

// Handles stack usage by a tf.If. It will convert the branch function
// signatures, and performs stack ops decomposition on them.
LogicalResult HandleIfOp(
    TF::IfOp if_op, ModuleOp module,
    const llvm::SmallDenseMap<Value, Value>& data_var_to_size_var,
    llvm::StringMap<PartitionedCallStackOpsInfo>*
        decomposed_partitioned_call_callees) {

				valBytes := rsc.Value
				rl := &route.RouteConfiguration{}
				_ = proto.Unmarshal(valBytes, rl)
				routes = append(routes, rl)
			}
			a.handleRDS(routes)
		default:
			if isMCP {
				a.handleMCP(resourceGvk, msg.Resources)
			}
		}

		// If we got no resource - still save to the store with empty name/namespace, to notify sync
		// This scheme also allows us to chunk large responses !