populateWithGenerated(patterns);
    (void)applyPatternsAndFoldGreedily(module, std::move(patterns));
  }
};

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeVariablesPass() {
  return std::make_unique<LegalizeVariablesPass>();
}

}  // namespace TFL

std::unique_ptr<OperationPass<func::FuncOp>> CreateLegalizeJaxRandomPass();

// Creates a pass which is responsible for legalizing TensorFlow variables to
// TensorFlow Lite variables.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeVariablesPass();

// Creates a pass which analyze the model whether it is safe to use
// native TFLite variables or not.
std::unique_ptr<OperationPass<ModuleOp>> CreateAnalyzeVariablesPass();

                                        pass_config.preserve_assert_op));
    pass_manager->addPass(mlir::TFL::CreateAnalyzeVariablesPass());
    pass_manager->addPass(mlir::TFL::CreateLegalizeVariablesPass());
    pass_manager->addPass(mlir::TFL::CreateLegalizeHashTablesPass());
    if (!pass_config.unfold_batch_matmul) {
      // Enable an optimization pass that transforms FC to BatchMatmul only when

}

def LegalizeVariablesPass : Pass<"tfl-legalize-variables-tf", "mlir::ModuleOp"> {
  let summary = "Legalize TensorFlow variables to TensorFlow Lite dialect.";
  let constructor = "CreateLegalizeVariablesPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

def LiftTfliteFlexOpsPass : Pass<"tfl-lift-tflite-flex-ops", "mlir::func::FuncOp"> {