signature_def_map,
      std::vector<std::string> signature_keys);

  // Runs calibration on `module_op` and returns a calibrated ModuleOp with
  // calibrated statistics embedded.
  absl::StatusOr<ModuleOp> Run(
      ModuleOp module_op,
      const ::stablehlo::quantization::QuantizationConfig& config) override;

 private:
  // Exports `module_op` to SavedModel at `dst_saved_model_path`. This is used

// pass.
std::unique_ptr<OperationPass<ModuleOp>> CreateTrimFunctionsPass();

std::unique_ptr<OperationPass<ModuleOp>> CreateTrimFunctionsPass(
    const std::vector<std::string>& trim_funcs_allowlist);

// Creates an instance of the TensorFlow Lite dialect PrepareCompositeFunctions
// pass.
std::unique_ptr<OperationPass<ModuleOp>> CreatePrepareCompositeFunctionsPass();

namespace mlir::quant::stablehlo {

// Represents a pair of `mlir::ModuleOp` and `tensorflow::SavedModelBundle`. The
// SavedModelBundle complements the imported ModuleOp by providing access to
// `tensorflow::Session` which may be useful when reading values from resources
// (e.g. `TF::VarHandleOp`s).
using ImportedMlirModuleOp =
    std::pair<OwningOpRef<ModuleOp>,
              std::unique_ptr<::tensorflow::SavedModelBundle>>;

// tf_executor dialect.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ConvertGraphdefToMlir(
    const GraphDef& graphdef, const GraphDebugInfo& debug_info,
    const GraphImportConfig& specs, mlir::MLIRContext* context);

// Given a Graph, returns a MLIR module containing the graph, expressed with
// tf_executor dialect.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ConvertGraphToMlir(

                                         StringPiece func_name);

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

  // Erases the tfr_module created.
  void Destroy();

 private:
  mlir::ModuleOp tfr_module_;
  mlir::PassManager pm_;

  GraphExportConfig export_confs_;
};

// Decomposes the NodeDef to a set of primitive ops according to the decompose

    "mlir::TFL::TFLDialect",
  ];
}

def LegalizeHloToTfPass : Pass<"tf-legalize-hlo", "ModuleOp"> {
  let summary = "Legalize from MHLO to the TF dialect";
  let dependentDialects = ["TF::TensorFlowDialect"];
  let constructor = "mlir::odml::CreateLegalizeHloToTfPass()";
}

def LegalizeHloToTfLitePass : Pass<"tfl-legalize-hlo", "mlir::ModuleOp"> {
  let summary = "Legalize from MHLO to the TFLite dialect";

  MLIRContext ctx_;
};

// Parses `module_op_str` and returns the resulting `ModuleOp`.
ModuleOp ParseModuleOp(const StringRef module_op_str, Block& block,
                       MLIRContext& ctx) {
  const LogicalResult parse_result =
      parseSourceString(module_op_str, &block, ParserConfig(&ctx));
  EXPECT_TRUE(succeeded(parse_result));

  return cast<ModuleOp>(block.front());
}

TEST_F(TfSavedModelTest,

std::unique_ptr<OperationPass<ModuleOp>> CreateQuantizeWeightsPass(
    const tensorflow::quantization::QuantizationOptions& quant_options);

// Propagate quantized type through allowed ops.
std::unique_ptr<OperationPass<ModuleOp>> CreatePropagateQuantizeTypePass();

// Create a pass that inserts dump tensor to quantizable layer's output.
std::unique_ptr<OperationPass<ModuleOp>> CreateAddDumpTensorOpPass(

SessionInitializerOp GetSessionInitializerOp(ModuleOp module_op);

// Returns the exported name for the session initializer function.
SmallVector<StringRef, 2> GetSessionInitializerExportedName(ModuleOp module_op);

// Returns initializer function ops. These functions' symbols are in the
// "initializers" attribute of the session initializer op.
SmallVector<func::FuncOp, 2> GetInitializerFunctions(ModuleOp module_op);

  "ModuleOp"> {
  let summary = "Freezes read only variables";

  let description = [{
    Test pass for freezing read only variables in the graph.
  }];

  let constructor = "::mlir::tf_test::CreateFreezeVariableTestPass()";
}

def InitTextFileToImportTestPass : Pass<"tf-init-text-file-to-import-test",
  "ModuleOp"> {