graph = ops.get_default_graph()
    for op in graph.get_operations():
      if op.type == '_Retval':
        expected_node_name = op.name
        if op.get_attr('tf_saved_model.index_path') is not None:
          index_path_name = op.get_attr('tf_saved_model.index_path')[0]
          index_path_name = index_path_name.decode('utf-8').split(':')[0]
          try:

      return false;
    }
    // FusedMatMul kernel does not support grad_a/grad_b attrs
    if ((matmul->hasAttr("grad_a") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) ||
        (matmul->hasAttr("grad_b") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {
        diag << "FusedMatMul kernel does not support grad_a/grad_b attrs";

  // Check that there are no duplicated exported_names.
  DenseMap<StringRef, Operation *> exported_name_to_op;
  for (auto &op : module) {
    auto attr = op.getAttr(exported_names_ident);
    if (!attr) continue;
    // If this verifier is called before we verify the
    // 'tf_saved_model.exported_names' attribute, then it might be invalid.

        graph_func.getLoc(),
        graph_func->getAttrOfType<StringAttr>(SymbolTable::getSymbolAttrName())
            .getValue(),
        ftype);

    func->setAttrs(graph_func->getAttrs());

    llvm::SmallVector<Type> arg_types;
    llvm::SmallVector<Type> res_types;
    llvm::SmallVector<DictionaryAttr> arg_attrs;
    llvm::SmallVector<DictionaryAttr> res_attrs;

    if (auto float_attr = mlir::dyn_cast_or_null<FloatAttr>(attr.second)) {
      fbb.Float(attr.first.data(), float_attr.getValue().convertToFloat());
    } else if (auto int_attr =
                   mlir::dyn_cast_or_null<IntegerAttr>(attr.second)) {
      fbb.Int(attr.first.data(), int_attr.getInt());
    } else if (auto bool_attr = mlir::dyn_cast_or_null<BoolAttr>(attr.second)) {
      fbb.Bool(attr.first.data(), bool_attr.getValue());

                        ctx->GetAttr("Tconstants", &constant_types));
  std::vector<int> constants(constant_types.size());
  std::iota(constants.begin(), constants.end(), 0);
  return constants;
}

std::vector<int> ResourcesVector(OpKernelConstruction* ctx) {
  DataTypeVector constant_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Tconstants", &constant_types));

  auto* defining_op = op->getOperand(operand_index).getDefiningOp();
  for (auto attr : kQuantizationAxisAttrs) {
    if (defining_op->hasAttr(attr)) {
      return defining_op->getAttr(attr);
    }
  }
  // Not found.
  return rewriter.getI64IntegerAttr(-1);
}

LogicalResult CheckIfAttrIs8Bit(const std::string& attr, Operation* op,
                                bool& is_8_bit) {

    cc_type v;                                                                 \
    auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelConstruction*>(ctx); \
    absl::Status s = cc_ctx->GetAttr(attr_name, &v);                           \
    ::tensorflow::Set_TF_Status_from_Status(status, s);                        \
    if (s.ok()) {                                                              \

                            FuncAttr attr) {
  func.eraseBody();
  func.addEntryBlock();
  func->setAttr(kTFImplements, attr);
  OpBuilder builder(func.getBody());
  std::string custom_option_buffer;
  if (failed(CreateNgramsCustomOption(func, attr.getAttrs(),
                                      custom_option_buffer))) {
    return failure();
  }
  auto op = builder.create<CustomOp>(

             << ")";
    }
  }
  return success();
}

void GraphOp::print(OpAsmPrinter &p) {
  p << ' ';
  p.printRegion(getOperation()->getRegion(0));
  p.printOptionalAttrDict(getOperation()->getAttrs());
}

ParseResult GraphOp::parse(OpAsmParser &parser, OperationState &result) {
  llvm::SMLoc loc = parser.getCurrentLocation();

  // Parse the body region.
  Region &body = *result.addRegion();