if ev.Kind() == trace.EventStackSample {
				totalTraceSamples++
				if hogRegion != nil && ev.Goroutine() == hogRegion.Goroutine() {
					traceSamples++
					var fns []string
					ev.Stack().Frames(func(frame trace.StackFrame) bool {
						if frame.Func != "runtime.goexit" {
							fns = append(fns, fmt.Sprintf("%s:%d", frame.Func, frame.Line))
						}
						return true
					})
					stack := strings.Join(fns, "|")

			// goroutine, because it represents its immutable start point.
			if g.Name == "" {
				stk := st.Stack
				if stk != NoStack {
					var frame StackFrame
					var ok bool
					stk.Frames(func(f StackFrame) bool {
						frame = f
						ok = true
						return true
					})
					if ok {
						// NB: this PC won't actually be consistent for

		return s
	}
	return s
}

func checkStack(e *errAccumulator, stk trace.Stack) {
	// Check for non-empty values, but we also check for crashes due to incorrect validation.
	i := 0
	stk.Frames(func(f trace.StackFrame) bool {
		if i == 0 {
			// Allow for one fully zero stack.
			//
			// TODO(mknyszek): Investigate why that happens.
			return true
		}
		if f.Func == "" || f.File == "" || f.PC == 0 || f.Line == 0 {

    } else if (stack_traces_.contains(debug_info_key)) {
      stack_trace = stack_traces_.at(debug_info_key);
    } else {
      DVLOG(1) << "No stack trace for " << node.name();
    }

    llvm::SmallVector<mlir::Location, 4> locations;

    if (stack_trace != nullptr) {
      DVLOG(1) << "Stack available for " << node.name();

  %2 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %3 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>
  %4 = "tf.StackV2"(%3) {elem_type = f32, stack_name = "s"} : (tensor<i32>) -> tensor<!tf_type.resource>
  %5:5 = "tfl.while"(%2, %3, %2, %4, %0) ({
  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<!tf_type.resource>, %arg5: tensor<f32>):

func.func @ignore_stack_ops(%arg0: tensor<i32>) -> () {
  "tf_device.cluster"() ({
    // CHECK: "tf.StackV2"
    // CHECK-NOT: _xla_outside_compilation
    %0 = "tf.StackV2"(%arg0) {elem_type = f32, stack_name = "s"} : (tensor<i32>) -> tensor<!tf_type.resource>
    tf_device.return
  }) {allow_soft_placement = true, num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> ()
  func.return
}

		}
		state.varParts[topSlot.N] = append(state.varParts[topSlot.N], SlotID(i))
	}

	// Recreate the LocalSlot for each stack-only variable.
	// This would probably be better as an output from stackframe.
	for _, b := range f.Blocks {
		for _, v := range b.Values {
			if v.Op == OpVarDef {
				n := v.Aux.(*ir.Name)
				if ir.IsSynthetic(n) {
					continue
				}

		s.vars[memVar] = s.newValue1Apos(ssa.OpVarLive, types.TypeMem, deferBitsTemp, s.mem(), false)
	}

	var params *abi.ABIParamResultInfo
	params = s.f.ABISelf.ABIAnalyze(fn.Type(), true)

	// The backend's stackframe pass prunes away entries from the fn's
	// Dcl list, including PARAMOUT nodes that correspond to output
	// params passed in registers. Walk the Dcl list and capture these

    Arg<TF_Int32Tensor, [{The maximum size of the stack if non-negative. If negative, the stack
size is unlimited.}]>:$max_size,

    TypeAttr:$elem_type,
    DefaultValuedOptionalAttr<StrAttr, "\"\"">:$stack_name
  );

  let results = (outs
    Res<TF_ResourceTensor, [{The handle to the stack.}], [TF_StackAlloc]>:$handle
  );
}