namespace mlir {
namespace TF {

using ::tensorflow::kValidDeviceTypes;

LogicalResult HasValidCompilationAndReplicationAttributes(Operation& op) {
  auto replicate_attr = op.getAttrOfType<StringAttr>(kReplicationInfoAttr);
  auto compile_attr = op.getAttrOfType<StringAttr>(kCompileDeviceTypeAttr);
  if (!replicate_attr && !compile_attr) return success();
  if (!replicate_attr || !compile_attr)

      }
    } else if (StringAttr attr = arg.getDefiningOp()->getAttrOfType<StringAttr>(
                   kDeviceAttr)) {
      return op_device == attr;
    }
    // Fold tf.Identity when arg device is not defined.
    return true;
  };

  func_op.walk([&should_fold_op_func](TF::IdentityOp op) {
    StringAttr op_device = op->getAttrOfType<StringAttr>(kDeviceAttr);
    if (should_fold_op_func(op.getOperand(), op_device)) {

  double real_multiplier = scale_product / o_spec.getScale();
  output_multipliers->push_back(QuantizeMultiplier(real_multiplier));

  // output ranges
  auto min = rop->getAttrOfType<FloatAttr>("min");
  auto max = rop->getAttrOfType<FloatAttr>("max");
  output_ranges->push_back(CalculateQuantizedRange(
      o_spec.getScale(), o_spec.getZeroPoint(),
      (min ? std::optional<double>(min.getValueAsDouble()) : std::nullopt),

  StringAttr shared_name_attr = op->getAttrOfType<StringAttr>(kSharedNameAttr);
  if (shared_name_attr) {
    auto shared_name = StringRef(shared_name_attr.getValue());
    if (!shared_name.empty()) {
      return shared_name;
    }
  }
  // Attempt to parse "_class" attribute if there is no "shared_name"
  // attribute.
  ArrayAttr classes_attr = op->getAttrOfType<ArrayAttr>(kClassAttr);
  if (!classes_attr) {

  // The sizes of indices, shared_name and container must be equal.
  int32_t indices_size =
      get_resource_op->getAttrOfType<mlir::ArrayAttr>("indices").size();
  int32_t shared_name_size =
      get_resource_op->getAttrOfType<mlir::ArrayAttr>("shared_name").size();
  int32_t container_size =
      get_resource_op->getAttrOfType<mlir::ArrayAttr>("container").size();

  if (!(indices_size == shared_name_size &&

ResourceHandle GetResourceHandle(Operation *op) {
  llvm::StringRef device;
  if (auto attr = op->getAttrOfType<StringAttr>("device")) {
    device = attr.getValue();
  }

  llvm::StringRef container;
  if (auto attr = op->getAttrOfType<StringAttr>("container")) {
    container = attr.getValue();
  }

  llvm::StringRef shared_name;
  if (auto attr = op->getAttrOfType<StringAttr>("shared_name")) {
    shared_name = attr.getValue();
  }

}

mlir::LogicalResult ExtractTfVersions(mlir::ModuleOp module,
                                      VersionDef* versions) {
  versions->Clear();
  auto version_attr =
      module->getAttrOfType<mlir::DictionaryAttr>("tf.versions");
  if (!version_attr) return mlir::failure();

  auto producer =
      mlir::dyn_cast_or_null<mlir::IntegerAttr>(version_attr.get("producer"));
  if (!producer) return mlir::failure();

inline bool TFDataFormatIsNHWC(Operation *op) {
  auto attr = op->getAttrOfType<StringAttr>("data_format");
  return !attr || attr.getValue() == "NHWC";
}

// Returns true if the given TensorFlow op does not have a `data_format`
// attribute (then default to "NDHWC"), or its `data_format` attribute is
// "NDHWC".
inline bool TFDataFormatIsNDHWC(Operation *op) {
  auto attr = op->getAttrOfType<StringAttr>("data_format");

  if (!call_op->hasAttr(kOriginalStablehloEntryFunctionAttrName)) {
    return;
  }

  const auto original_function_name = call_op->getAttrOfType<StringAttr>(
      kOriginalStablehloEntryFunctionAttrName);
  const auto current_function_name = call_op->getAttrOfType<FlatSymbolRefAttr>(
      TF::kStablehloEntryFunctionAttrName);

  if (!original_function_name || !current_function_name) {
    return;
  }

  StringRef device;
  if (auto attr = op->getAttrOfType<StringAttr>("device")) {
    device = attr.getValue();
  }

  StringRef container;
  if (auto attr = op->getAttrOfType<StringAttr>("container")) {
    container = attr.getValue();
  }

  StringRef shared_name;
  if (auto attr = op->getAttrOfType<StringAttr>("shared_name")) {
    shared_name = attr.getValue();
  }