tensorflow::SerializeMlirModule(module_for_func.get());
}

// Returns whether `op` or ops nested in `op` are outside compiled.
bool HasOutsideCompilationNested(Operation* op) {
  return op
      ->walk([&](Operation* walked_op) {
        if (op == walked_op) return WalkResult::advance();
        if (walked_op->hasAttr(kXlaOutsideCompilationAttr)) {
          return WalkResult::interrupt();
        }

  std::vector<mlir::func::FuncOp> to_delete_funcs;
  module.walk([&](mlir::vhlo::ReduceOpV1 reduce_op) {
    auto body = symbol_table.lookup<mlir::func::FuncOp>(
        reduce_op->getAttr("body").cast<mlir::FlatSymbolRefAttr>().getValue());
    InlineVhloOpRegion(reduce_op.getBody(), body);
    reduce_op->removeAttr("body");
    to_delete_funcs.push_back(body);
  });
  module.walk([&](mlir::vhlo::ReduceWindowOpV1 reduce_window_op) {

	// Populate "rval" with what we've computed.
	rval.Slots = state.slots
	rval.VarSlots = state.varSlots
	rval.Vars = state.vars
	rval.LocationLists = state.lists
}

// liveness walks the function in control flow order, calculating the start
// and end state of each block.
func (state *debugState) liveness() []*BlockDebug {
	blockLocs := make([]*BlockDebug, state.f.NumBlocks())
	counterTime := int32(1)

    return false;
  }
  // Detect summaries by looking for key Ops in the graph. It would be better to
  // do this via operator attributes rather than looking for a specific op.
  WalkResult walk_result = module.walk([&](Operation* op) -> WalkResult {
    if (llvm::isa<TF::WriteSummaryOp>(op)) return WalkResult::interrupt();
    return WalkResult::advance();
  });
  if (walk_result.wasInterrupted()) {

	return expandGoroot(fname)
}

// writeDirFileTables emits the portion of the DWARF line table
// prologue containing the include directories and file names,
// described in section 6.2.4 of the DWARF 4 standard. It walks the
// filepaths for the unit to discover any common directories, which
// are emitted to the directory table first, then the file table is
// emitted after that.

//
// When the hashtable grows, we allocate a new array
// of buckets twice as big. Buckets are incrementally
// copied from the old bucket array to the new bucket array.
//
// Map iterators walk through the array of buckets and
// return the keys in walk order (bucket #, then overflow
// chain order, then bucket index).  To maintain iteration
// semantics, we never move keys within their bucket (if
// we did, keys might be returned 0 or 2 times).  When

	// like stack printing, where it's better to get incomplete information than
	// to crash. This is also used in situations where everything may not be
	// stopped nicely and the stack walk may not be able to complete, such as
	// during profiling signals or during a crash.
	//
	// If neither unwindPrintErrors or unwindSilentErrors are set, unwinding
	// performs extra consistency checks and throws on any error.

		nil, // No need for selectable fields
	)

	return func(new, old *unstructured.Unstructured) {
		_ = strategy.ValidateUpdate(context.TODO(), new, old)
	}, nil
}

// Recursively walks the provided directory and parses the YAML files into
// unstructured objects. If there are more than one object in a single file,
// they are all added to the returned slice.

		// one of source/target is s3, skip delete marker and all versions under the same object name.
		s3Type := r.Target.Type == BatchJobReplicateResourceS3 || r.Source.Type == BatchJobReplicateResourceS3

		if err := api.Walk(ctx, r.Source.Bucket, r.Source.Prefix, walkCh, WalkOptions{
			Marker:   lastObject,
			Filter:   selectObj,
			AskDisks: walkQuorum,
		}); err != nil {
			cancel()

			fmt.Printf("Begin processing block %v\n", b)
		}
		s.curBlock = b
		s.startRegsMask = 0
		s.usedSinceBlockStart = 0

		// Initialize regValLiveSet and uses fields for this block.
		// Walk backwards through the block doing liveness analysis.
		regValLiveSet.clear()
		for _, e := range s.live[b.ID] {
			s.addUse(e.ID, int32(len(b.Values))+e.dist, e.pos) // pseudo-uses from beyond end of block