TestFile dir

    BuildOperationNotificationFixture(TestFile dir) {
        this.dir = dir
    }

    def op(Class<?> detailsClass, Map<String, String> details = [:]) {
        def found = recordedOps.findAll { op ->
            return op.detailsType == detailsClass.name && op.details.subMap(details.keySet()) == details
        }
        assert found.size() == 1
        return found.first()
    }

  for (Operation& op : block.without_terminator()) {
    StringRef shared_name = GetSharedName(&op);
    if (shared_name.empty() || !IsHashTableOp(&op)) continue;
    if (!IsResourceInitialized(module_op, &op)) continue;

    auto it =
        shared_name_to_arg_idx.insert({shared_name, block.getNumArguments()});
    if (it.second) {
      auto resource_type = op.getResult(0).getType();

  void runOnOperation() override;
};

// Checks if the island op contains a resource op like Variable or Hashtable
// and returns that resource op. Otherwise, returns null.
Operation* GetResourceOp(Operation* op) {
  // Check if the island has only one block thats contain two ops, including
  // one resource op and one Yield op.
  auto island_op = llvm::dyn_cast_or_null<IslandOp>(op);
  if (!island_op || !island_op.getBody().hasOneBlock()) return nullptr;

TF_CAPI_EXPORT extern void TFE_DeleteOp(TFE_Op* op);

// Returns the op or function name `op` will execute.
//
// The returned string remains valid throughout the lifetime of 'op'.
TF_CAPI_EXPORT extern const char* TFE_OpGetName(const TFE_Op* op,
                                                TF_Status* status);
TF_CAPI_EXPORT extern TFE_Context* TFE_OpGetContext(const TFE_Op* op,

    int quantize_operand_num = quant_op.second;

    Type expressed_type = op.getResult().getType();
    Type cast_type = quant_type.castFromExpressedType(expressed_type);

    // Insert DQ-op if it does not exist yet. Otherwise, just rewire without
    // creating a new DQ-op.
    for (auto connected_op : op->getUsers()) {
      auto q_op =
          llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(connected_op);

	clone := func() {
		if !cloned {
			cloned = true
			op = &Op{}
			*op = *orig
		}
	}

	pCloned := false
	for i := range op.plist {
		if s := w.walkP(&op.plist[i]); s != &op.plist[i] {
			if !pCloned {
				pCloned = true
				clone()
				op.plist = make([]P, len(orig.plist))
				copy(op.plist, orig.plist)
			}
			op.plist[i] = *s
		}
	}

	return op
}

func main() {
	var ww M
	w := &ww

	op = strings.Replace(op, "(*stsAPIHandlers)", "sts", 1)
	op = strings.Replace(op, "(*peerS3Server)", "s3", 1)
	op = strings.Replace(op, "ClusterCheckHandler", "health.Cluster", 1)
	op = strings.Replace(op, "ClusterReadCheckHandler", "health.ClusterRead", 1)
	op = strings.Replace(op, "LivenessCheckHandler", "health.Liveness", 1)
	op = strings.Replace(op, "ReadinessCheckHandler", "health.Readiness", 1)
	op = strings.Replace(op, "-fm", "", 1)
	return op
}

  SmallVector<NamedAttribute> attrs{op->getAttrs()};
  attrs.push_back(call_target);
  auto custom_call = rewriter.create<mlir::stablehlo::CustomCallOp>(
      op->getLoc(), op->getResultTypes(), op->getOperands(), attrs);
  rewriter.replaceOp(op, custom_call.getResults());
  return success();
}

}  // namespace

template <typename OpTy>

  func.setPrivate();
}

void RunOnWhile(TF::WhileOp while_op) {
  Operation* op = while_op.getOperation();
  // Create new TFL While op that will be used to replace TF While op.
  auto new_op = OpBuilder(op).create<TFL::WhileOp>(
      op->getLoc(), op->getResultTypes(), op->getOperands(),
      while_op.getIsStateless());
  Location loc = while_op->getLoc();

    for (auto& op : graph->operators) {
      for (int idx = 0; idx < op->inputs.size(); idx++) {
        if (op->inputs[idx] < 0) {
          continue;
        }
        const auto& tensor = graph->tensors[op->inputs[idx]];
        auto* expected_tensor = FindMatchingExpectedTensor(
            *expected_graph, expected_model, model, *op, idx);
        if (!expected_tensor) {
          continue;