EXPECT_TRUE(MessageDifferencer::Equals(method, Method::default_instance()));
}
constexpr absl::string_view kModuleDotWeightOnlyPtq = R"mlir(
  module {
    func.func @main(%arg0: tensor<?x2xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<?x2xf32>) {
      %0 = stablehlo.constant dense<[-0.211145893, -0.708605706]> : tensor<2xf32>

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: same_scale_after_composite
  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  func.func private @same_scale_after_composite(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<3x1xf32> {

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/RegionUtils.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"

namespace mlir {
namespace TF {
namespace {

bool IsResourceType(Type type) {
  if (auto tensor_type = mlir::dyn_cast<TensorType>(type)) {

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"

namespace mlir {
namespace TF {

// Used as a lattice value.
struct ResourceConstructingOps {

    tbl_outs = [
        (
            ["-gen-op-decls"],
            "ir/tf_saved_model.h.inc",
        ),
        (
            ["-gen-op-defs"],
            "ir/tf_saved_model.cc.inc",
        ),
        (
            ["-gen-dialect-doc"],
            "g3doc/tf_saved_model.md",
        ),
    ],
    tblgen = "@llvm-project//mlir:mlir-tblgen",

    // constant ops.
    pass_manager->addPass(mlir::tf_saved_model::CreateFreezeGlobalTensorsPass(
        /*allow_mutable_tensors=*/pass_config.enable_tflite_variables));
  }

  if (!saved_model_dir.empty()) {
    // This pass 'freezes' tf saved model asset ops and inlines as string values
    // in a format of the tf constant op.
    pass_manager->addPass(
        mlir::tf_saved_model::CreateFreezeAssetsPass(saved_model_dir.str()));
  }

namespace errors = tensorflow::errors;
namespace tfl = mlir::TFL;

namespace {

constexpr char kScatterRegionFuncName[] = "update_computation_func_name";

using ::mlir::tf_saved_model::kTfSavedModelExportedNamesAttr;
using ::mlir::tf_saved_model::kTfSavedModelIndexPathAttr;
using ::tflite::IsValidBufferOffset;

// Create the MLIR NamedLoc location corresponding to a given tensor

// to the save function and returns the file_prefix argument (typed
// `tensor<!tf_type.string>`). The file_prefix argument, which can be identified
// if the "tf_saved_model.index_path" attribute has "__tf_file_prefix", will be
// reused if it already exist in @main. Otherwise a new file prefix argument
// will be created. @tf_quant__save function will be erased.
//

    StaticRangePtqPreset static_range_ptq_preset = 1;
    WeightOnlyPtqPreset weight_only_ptq_preset = 7;
  }

  // TF SavedModel specific information for the input model.
  TfSavedModelConfig tf_saved_model = 2;

  // Configures the graph transformation pipeline for quantization.
  PipelineConfig pipeline_config = 3;

  QuantizationSpecs specs = 4;

  // Configures the quantization debugger.

      self-contained.
      For example, this would transform

      "tf_saved_model.global_tensor"() { sym_name = "v" ... }
      func @f(%arg0 {tf_saved_model.bound_input = @v}) {
        %1 = "tf.ReadVariableOp"(%arg0)
        ...
      }

      to

      func @f(%arg0 {tf_saved_model.bound_input = @v}) {
        %0 = "tf.VarHandleOp"(sym_name = "v")
        %1 = "tf.ReadVariableOp"(%0)