*list->add_tensor() = tensor;
    } else if (auto attr = mlir::dyn_cast<mlir::FlatSymbolRefAttr>(a)) {
      AttrValue attr_val;
      TF_RETURN_IF_ERROR(ConvertAttribute(attr, &attr_val));
      *list->add_func() = attr_val.func();
    } else if (auto attr = mlir::dyn_cast<mlir::TypeAttr>(a)) {
      AttrValue attr_val;
      // For type attributes, we only propagate the element type.
      mlir::Type elt_type = attr.getValue();

    for (const auto& name_and_value : node_def.attr()) {
      const std::string& attr_name = name_and_value.first;
      const tensorflow::AttrValue& attr_value = name_and_value.second;
      absl::StatusOr<Attribute> mlir_attr =
          tensorflow::ConvertAttributeValue(attr_value, &builder);
      if (!mlir_attr.ok()) {
        return emitError(loc, mlir_attr.status().message());
      }

                                     IntegerAttr attr_value, Operation* op,
                                     ModuleOp& module) {
  const auto ex_attr_value = module->getAttrOfType<IntegerAttr>(attr_name);
  if (ex_attr_value == nullptr) {
    module->setAttr(attr_name, attr_value);
    return success();
  }
  if (ex_attr_value == attr_value) {
    return success();
  }

                                  const void* proto, size_t proto_len,
                                  TF_Status* status) {
  tensorflow::AttrValue attr_value;
  if (!attr_value.ParseFromArray(proto, proto_len)) {
    status->status = InvalidArgument(
        "Unparseable AttrValue proto passed to "
        "TF_FunctionSetAttrValueProto");
    return;
  }

  auto fdef_or = func->record->mutable_fdef();

  }
  return true;
}

bool HasSymbolRefAttr(Operation* op) {
  for (const auto& attr : op->getAttrs()) {
    Attribute attr_value = attr.getValue();
    if (mlir::isa<SymbolRefAttr>(attr_value)) {
      return true;
    } else if (auto array_attr = mlir::dyn_cast<ArrayAttr>(attr_value)) {
      if (!array_attr.empty() &&
          mlir::isa<SymbolRefAttr>(*array_attr.begin())) {
        return true;
      }
    }

                             const void* proto, size_t proto_len,
                             TF_Status* status) {
  tensorflow::AttrValue attr_value;
  if (!attr_value.ParseFromArray(proto, proto_len)) {
    status->status =
        tensorflow::errors::InvalidArgument("Unparseable AttrValue proto");
    return;
  }
  if (op == nullptr) {
    status->status = tensorflow::errors::InvalidArgument(

      auto attr_name = signature.getArgAttrOfType<StringAttr>(
          operand.index(), kAttrArgumentNameAttr);
      auto attr_value =
          signature.getArgAttr(operand.index(), kAttrArgumentDefaultAttr);
      arg_attrs->push_back(
          rewriter.getNamedAttr(attr_name.getValue(), attr_value));
      continue;
    }

    // The index is valid for the call_op.
    Value input = call_op.getOperand(operand.index());

#include "tensorflow/compiler/mlir/quantization/tensorflow/python/type_casters.h"  // IWYU pragma: keep
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace py = pybind11;

namespace {

using ::tensorflow::SignatureDef;
using ::tensorflow::quantization::ExportedModel;

      LIST_CASE(tensor, TF_ATTR_FUNC);
#undef LIST_CASE
      break;

    case tensorflow::AttrValue::kPlaceholder:
      *list_size = -1;
      *total_size = -1;
      break;

    case tensorflow::AttrValue::kFunc:
      *list_size = -1;
      *total_size = -1;
      break;

    case tensorflow::AttrValue::VALUE_NOT_SET:
      status->status = absl::InvalidArgumentError(

                                                     .begin())>::value>::type>
Status SetTypeAttribute(absl::string_view name, ContainerT types,
                        AttrValueMap* values) {
  AttrValue value;
  auto& type_list = *value.mutable_list();
  for (auto type : types) {
    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertScalarTypeToDataType(type, &dtype));
    type_list.add_type(dtype);
  }