}

void TFE_GetExecutedOpNames(TFE_Context* ctx, TF_Buffer* buf,
                            TF_Status* status) {
  const std::vector<std::string>& op_names =
      tensorflow::unwrap(ctx)->GetLoggedOpsTestonly();

  std::ostringstream op_names_oss;
  for (const auto& op : op_names) {
    op_names_oss << op << ", ";
  }
  const std::string& op_names_str = op_names_oss.str();

                        false /* copy_names */));
}

Scope Scope::WithOpNameImpl(const string& op_name) const {
  if (impl()->single_use_scope()) {
    UpdateStatus(errors::InvalidArgument("Cannot set op name ", op_name,
                                         " on this scope"));
    return *this;
  }
  return Scope(new Impl(*this, Impl::Tags::OpName(), impl()->name_, op_name));
}

 private:
  friend class GraphContext;  // For access to op_.
  TF_Graph* g_;
  std::unique_ptr<TF_OperationDescription> op_;
  // Hold `op_type` and `op_name` till both are available since we need both
  // to build a graph operation.
  string op_type_;
  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

// GraphContext wraps a TF_Graph modeling a single function and manages the

  attrs_[attr_name] = TypeAttr::get(mlir_type);
  return absl::OkStatus();
}

Status MlirAbstractOp::SetOpName(const char* const op_name) {
  // TODO(aminim): should we use a location?
  if (op_name_) {
    return FailedPrecondition("SetOpName called on already built op.");
  }
  op_name_ = op_name;
  return absl::OkStatus();
}

Status MlirAbstractOp::AddRef(Type type, Type* output_type) {

  if (!op_name_or_status.ok()) return false;

  auto op_name = std::move(op_name_or_status).value();
  if (op_name == "NextIteration")
    return mlir::isa<mlir::TF::TensorFlowRefType>(
        mlir::getElementTypeOrSelf(op->getOperand(0).getType()));

  if (op_name == "Enter" || op_name == "Exit" || op_name == "Switch" ||
      op_name == "Merge") {
    return mlir::isa<mlir::TF::TensorFlowRefType>(

    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_count",
    "Counts the attempts of legalization of ops", "op_name");

auto *mlir_failed_legalization_count = tensorflow::monitoring::Counter<2>::New(
    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count",
    "Counts the failure of legalization of ops", "op_name", "legality");

class LegalizeTF : public impl::LegalizeTFBase<LegalizeTF> {
 public:

  bool IsAllowListedOp(Operation *op) {
    std::string op_name = op->getName().getStringRef().str();
    if (IsAlwaysAllowlistedOp(op) || IsTfFakeQuantOp(op)) {
      return true;
    } else if (mode_ == kDefaultMode) {
      return QuantizableOpsInDefaultMode().count(op_name) > 0;
    } else if (mode_ == kLegacyIntegerMode) {
      return QuantizableOpsInLegacyMode().count(op_name) > 0;
    } else {

def _find_op(
    graph: ops.Graph, op_name: Optional[str]
) -> Optional[ops.Operation]:
  """Finds the operation with `op_name`.

  Args:
    graph: The graph to find from.
    op_name: Name of the node.

  Returns:
    The operation that corresponds to `op_name`. Returns None iff op_name is an
    empty string or None.

  Raises:
    ValueError: `op_name` is malformed.
  """
  if not op_name:

}
}  // namespace

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }
  registry_.insert({op_name, gradient_function_factory});
  return absl::OkStatus();
}

      }
    }

    return success();
  }

 private:
  // Parses TFLite Flex op's `custom_options` and returns the TF
  // `op_name` and TF dialect MLIR op attributes.
  static LogicalResult ParseCustomOption(
      StringRef custom_options, Location loc, std::string& op_name,
      SmallVectorImpl<NamedAttribute>& attributes,
      tensorflow::NodeDef& node_def) {