explicit MlirV1CompatGraphOptimizationPass(
      const MlirV1CompatOptimizationPassRegistry* registry =
          &MlirV1CompatOptimizationPassRegistry::Global())
      : registry_(registry) {}

  Status Run(const GraphOptimizationPassOptions& options) override;

 private:
  const MlirV1CompatOptimizationPassRegistry* registry_;
};

// -------------------------------------------------------------------------- //

                         << ", FallbackEnabled: " << num_passes_fallback_enabled
                         << ", Total: " << registry_->passes().size();
  }

  GraphDebugInfo debug_info;
  mlir::DialectRegistry registry;
  RegisterDialects(registry);
  mlir::MLIRContext context(registry);
  GraphImportConfig import_config;
  import_config.graph_as_function = true;
  import_config.control_outputs = *control_ret_node_names;

  /// the gradient function for 'op' exists in the registry.
  /// Note that 'func' can be null for ops that have registered no-gradient with
  /// the registry.
  /// Returns error status otherwise.
  Status Lookup(const string& op, GradFunc* func) const;

  /// Returns a pointer to the global gradient function registry.
  static GradOpRegistry* Global();

 private:

#else
  unsigned int uiNumberOfSuites;    /**< Number of registered suites in the registry. */
  unsigned int uiNumberOfTests;     /**< Total number of registered tests in the registry. */
  CU_pSuite    pSuite;              /**< Pointer to the 1st suite in the test registry. */
#endif
} CU_TestRegistry;
typedef CU_TestRegistry* CU_pTestRegistry;  /**< Pointer to a CUnit test registry. */

/*=================================================================

  context->appendDialectRegistry(registry_);
  context->loadAllAvailableDialects();

  // Run TAC passes.
  auto status = RunTacPasses(&module, options_.debug_mode);

  if (!status.ok()) {
    return status;
  }

  return exporter_->Export(module);
}

void TacModule::RegisterExtraDialects(mlir::DialectRegistry& registry) {
  registry.appendTo(registry_);
}
}  // namespace tac

namespace tensorflow {
namespace gradients {
TapeOperation::TapeOperation(AbstractOperation* parent_op, Tape* tape,
                             const GradientRegistry& registry)
    : AbstractOperation(kTape),
      parent_op_(parent_op),
      tape_(tape),
      registry_(registry) {
  // TODO(b/172003047): Consider making AbstractOperation RefCounted.
  // parent_op_->Ref();
}
void TapeOperation::Release() {

  const std::vector<const tac::TargetHardware*>& GetAvailableHardwares() const {
    return const_backends_;
  }

  // Registers all dialects in 'registry' with the module.
  // This to allow clients to register extra dialects required.
  void RegisterExtraDialects(mlir::DialectRegistry& registry);

 protected:
  // Adds TAC passes to the 'pass_manager'.
  virtual void AddTACPass(mlir::OpPassManager* pass_manager,

};

SymbolicGradientBuilder::SymbolicGradientBuilder(
    const Scope& scope, const ops::GradOpRegistry* registry,
    const std::vector<Output>& outputs, const std::vector<Output>& inputs,
    const std::vector<Output>& grad_inputs, std::vector<Output>* grad_outputs)
    : scope_(scope),
      registry_(registry),
      outputs_(outputs),
      inputs_(inputs),
      grad_inputs_(grad_inputs),

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();
}
Status GradientRegistry::Lookup(

//   // More complex gradient functions can use inputs/attrs etc. from the
//   // forward `op`.
//   return new AddGradientFunction;
// }
//
// Status RegisterGradients(GradientRegistry* registry) {
//   return registry->Register("Add", AddRegisterer);
// }
class GradientFunction {
 public:
  virtual Status Compute(AbstractContext* ctx,
                         absl::Span<AbstractTensorHandle* const> grad_outputs,