[](mlir::OpPassManager& pm, llvm::StringRef options,
         llvm::function_ref<mlir::LogicalResult(const llvm::Twine&)>
             errorHandler) {
        tensorflow::PopulateLowerToMlProgramAndHloPipeline(pm);
        return mlir::success();
      },
      [](llvm::function_ref<void(const mlir::detail::PassOptions&)>) {});
}

  }
  static Derived getChecked(function_ref<InFlightDiagnostic()> emitError,
                            MLIRContext* context, ArrayRef<StringAttr> attrs) {
    return Base::getChecked(emitError, context, attrs);
  }

  static Derived get(MLIRContext* context) { return get({}, context); }

  // TODO(fengliuai): fix the implementation
  static LogicalResult verify(function_ref<InFlightDiagnostic()> emitError,

// corresponding signature metadata populated in TFRTSavedModelSignatureInfo for
// the function.
Status MapFunctionSignaturesFromTFSavedModelMLIR(
    mlir::ModuleOp module,
    llvm::function_ref<void(const TFRTSavedModelSignatureInfo&)> map_fn);

}  // namespace tensorflow

  return std::make_pair(dtype, tensorflow::PartialTensorShape(dims));
}

}  // namespace

Status MapFunctionSignaturesFromTFSavedModelMLIR(
    mlir::ModuleOp module,
    llvm::function_ref<void(const TFRTSavedModelSignatureInfo&)> map_fn) {
  // Create bound inputs for each functions.
  mlir::SymbolTable symbol_table(module);
  tensorflow::Status status = absl::OkStatus();

    };

    auto dequant_values =
        mlir::cast<DenseIntOrFPElementsAttr>(input_values)
            .mapValues(FloatType::getF32(rewriter.getContext()),
                       llvm::function_ref<DequantizeFuncType>(dequantize_func));
    rewriter.replaceOpWithNewOp<TFL::ConstOp>(dequant_op, dequant_op.getType(),
                                              dequant_values);

    return success();
  }
};

using mlir::func::FuncOp;

// Run the TF XLA Bridge based on the input pipeline, which can be either TPU
// bridge pipeline or non TPU bridge pipeline.
tensorflow::Status RunTFXLABridge(
    ModuleOp module,
    llvm::function_ref<void(OpPassManager &pm)> pipeline_builder,
    llvm::StringRef module_name = llvm::StringRef(),
    llvm::StringRef dump_prefix = "tf_xla_bridge_v2") {
  // Explicitly check that the TensorFlow dialect can constant fold ops.

  for (tensorflow::NodeDef& node_def : *graph_def.mutable_node()) {
    func(node_def);
  }

  for (tensorflow::FunctionDef& function_def :
       *graph_def.mutable_library()->mutable_function()) {
    for (tensorflow::NodeDef& node_def : *function_def.mutable_node_def()) {
      func(node_def);
    }
  }
}

}  // namespace stablehlo::quantization

Status ConvertMlirFunctionToFunctionLibraryDef(mlir::func::FuncOp func,
                                               const GraphExportConfig& configs,
                                               FunctionDef* function_def);

}  // namespace v2
}  // namespace tf2xla
}  // namespace tensorflow

Status ConvertMlirFunctionToFunctionLibraryDef(mlir::func::FuncOp func,
                                               const GraphExportConfig& configs,
                                               FunctionDef* function_def);
}  // namespace tensorflow

  // Decomposes the op in the NodeDef to a set of primitive ops according to the
  // decompose library in the context. Wrap the decomposed result in a
  // FunctionDef.
  absl::StatusOr<FunctionDef> ExpandNode(const NodeDef& node_def,
                                         StringPiece func_name);

  // Runs the decompose passes on the user_module.
  Status DecomposeGraph(mlir::ModuleOp user_module);