const OperatorT& quant_op, int idx) {
  const auto& builtin_code =
      GetBuiltinCode(quant_model.operator_codes[quant_op.opcode_index].get());
  for (const auto& expected_op : expected_graph.operators) {
    const auto& op_code =
        expected_model.operator_codes[expected_op->opcode_index].get();
    const auto& expected_code = GetBuiltinCode(op_code);
    if (expected_code == builtin_code) {

import _ "example.com/printversion"
-- $WORK/auto/auto.go --
package auto
-- $WORK/auto/replacement-version/go.mod --
module example.com/version
-- $WORK/auto/replacement-version/version.go --
package version

const V = "v1.0.0-replaced"
-- $WORK/modules-1.14.txt --
# example.com/printversion v1.0.0
## explicit
example.com/printversion
# example.com/version v1.0.0 => ./replacement-version
example.com/version

    Block& block, const llvm::SmallDenseMap<Value, SizeInfo>& buffer_to_size) {
  auto old_terminator = block.getTerminator();
  auto new_outputs = llvm::to_vector<8>(old_terminator->getOperands());
  llvm::SmallVector<std::tuple<int64_t, int64_t, bool>, 8>
      output_buffer_to_size;
  for (auto retval : llvm::enumerate(old_terminator->getOperands())) {
    auto it = buffer_to_size.find(retval.value());
    if (it == buffer_to_size.end()) continue;

  auto loop_operands_n = UnpackResults(loop_operands_indexes_i);
  auto forward_res_nm2 = UnpackResults(forward_res_indexes_im2);
  auto forward_res_nm1 = UnpackResults(forward_res_indexes_im1);
  auto core_tpu_res_nm2 = UnpackResults(core_tpu_res_indexes_im2);
  auto non_tpu_res_nm1 = UnpackResults(non_tpu_res_indexes_im1);
  auto C_nm2 = new_while_op->getResult(C_index_im2);
  auto C_nm1 = new_while_op->getResult(C_index_im1);

}

void Exporter::UseOriginalFunctionNames(NodeDef& node_def) {
  if (!configs_.export_original_tf_func_name) return;

  auto& attrs = *node_def.mutable_attr();

  auto try_use_original_func_name = [this](std::string* name) {
    if (auto func = symbol_table_.lookup<FuncOp>(*name)) {
      if (auto original_func_name =
              func->getAttrOfType<mlir::StringAttr>("tf._original_func_name")) {

}

void Exporter::UseOriginalFunctionNames(NodeDef& node_def) {
  if (!configs_.export_original_tf_func_name) return;

  auto& attrs = *node_def.mutable_attr();

  auto try_use_original_func_name = [this](std::string* name) {
    if (auto func = symbol_table_.lookup<FuncOp>(*name)) {
      if (auto original_func_name =
              func->getAttrOfType<mlir::StringAttr>("tf._original_func_name")) {

  for (auto& input : inputs) {
    auto input_op = input.get().getDefiningOp();
    if (!input_op || !IsSupportedHostInputOp(input_op)) return false;
  }
  for (auto entry : llvm::enumerate(inputs)) {
    Value input = entry.value().get();
    auto ranked_type = mlir::dyn_cast<RankedTensorType>(input.getType());
    if (!ranked_type) return false;
    auto input_shape = ranked_type.getShape();

    return success();
  }

  auto stride_h = op.getStrideHAttr().getInt();
  auto stride_w = op.getStrideWAttr().getInt();
  auto dilation_h = op.getDilationHFactorAttr().getInt();
  auto dilation_w = op.getDilationWFactorAttr().getInt();

  // We don't have EXPLICIT PADDING in TfLite.
  auto paddings = op.getPadding();
  tensorflow::Padding padding;

    return rewriter.notifyMatchFailure(op, "zero_points must be constant");
  }

  auto original_element_type = getElementTypeOrSelf(original_type);
  if (!mlir::isa<TF::Qint8Type, TF::Qint32Type>(original_element_type)) {
    return rewriter.notifyMatchFailure(
        op, "Quantized type must be qint8 or qint32.");
  }
  auto storage_type = GetIntTypeFromTFQint(original_element_type);

  // has the property that if there is an edge from SCC1->SCC2, SCC1 is visited
  // after SCC2, i.e., the graph is traversed bottom up just the way we want.
  auto scc_begin = llvm::scc_begin(&call_graph);
  auto scc_end = llvm::scc_end(&call_graph);
  for (auto& scc : make_range(scc_begin, scc_end)) {
    // Each SCC node is a collection of callgraph nodes that form a cycle. We