void runOnOperation() override {
    MLIRContext *context = &getContext();
    ModuleOp module_op = getOperation();
    SmallVector<func::FuncOp, 4> original_func;
    for (auto func_op : module_op.getOps<func::FuncOp>()) {
      original_func.push_back(func_op);
    }
    for (auto func_op : original_func) {
      std::optional<llvm::StringMap<FunctionMetadata>> metadatas =
          GetFunctionMetadatas(func_op);

        sources.hasDependencyAlias('foo', 'getFoo', "with version <b>1.0</b>")
        sources.hasDependencyAlias('fooBaz', 'getFooBaz', "with version <b>{strictly [1.0, 2.0[; prefer 1.2}</b>")
        sources.hasDependencyAlias('bar', 'getBar', "with version reference <b>barVersion</b>")
        sources.hasDependencyAlias('boo', 'getBoo', "with <b>no version specified</b>")

        sources.hasPlugin('fooPlugin', 'getFooPlugin', "with version <b>1.0</b>")

  TF_RETURN_IF_ERROR(importer.ImporterBase::ConvertDeferredFunctions());

  // Mark main function public, others private.
  for (auto function : module.get().getOps<mlir::func::FuncOp>()) {
    auto visibility = function.getName() == graph_func_name
                          ? mlir::func::FuncOp::Visibility::Public
                          : mlir::func::FuncOp::Visibility::Private;

      device_mgr_.get(), tensorflow::Env::Default(), /*config=*/nullptr,
      version_or.value(), flib_def_.get(), tensorflow::OptimizerOptions());
  params_.function_library = pflr_->GetFLR(device_->name());
  return success();
}

// Returns true if the given type is a ranked tensor type with static or
// bounded dimensions.
bool IsBounded(Type ty) {

  // populates `op_side_effect_map_`.
  explicit OpSideEffectCollector(ModuleOp module) {
    symbol_table_collection_.getSymbolTable(module);
    for (auto func : module.getOps<func::FuncOp>()) {
      CollectOpSideEffects(func);
    }
  }

  // Returns op-based side effects by resource ID for `op`.
  const SideEffectsByResourceId& GetSideEffectsForOp(Operation* op) const {

    q_op->replaceAllUsesWith(new_qcast);
    return success();
  }
};

bool PrepareQuantizePass::ContainsQuantizeOps(func::FuncOp func) {
  for (const auto& op : func.getOps()) {
    if (llvm::isa<quantfork::DequantizeCastOp>(op)) return true;
  }
  return false;
}

using PrepareQuantStats =
    quant::ConvertStatsToQDQs<quantfork::QuantizeCastOp,

        << ") as consumer - intentional?";
  });
}

bool PrepareQuantizePass::ContainsQuantizeOps(func::FuncOp func) {
  for (const auto& op : func.getOps()) {
    if (llvm::isa<quantfork::DequantizeCastOp>(op)) return true;
  }
  return false;
}

using PrepareQuantStats =
    quant::ConvertStatsToQDQs<quantfork::QuantizeCastOp,

  ASSERT_TRUE(module_op);

  auto func_op = module_op->lookupSymbol<func::FuncOp>("quantize");
  ASSERT_THAT(func_op, NotNull());

  auto uniform_quantize_op =
      *func_op.getOps<::mlir::stablehlo::UniformQuantizeOp>().begin();
  Value result = uniform_quantize_op.getResult();
  EXPECT_THAT(GetElementType(result), NotNull());
}

}  // namespace
}  // namespace quant

};

CoarseningAnalysis::CoarseningAnalysis(GraphOp graph) {
  // As an initial step, construct a merged island for each island in the
  // graph.
  for (IslandOp island : graph.SingleBlock::getBody()->getOps<IslandOp>())
    merged_islands_.push_back(MergedIsland(island));

  // Record the mapping from the island to the merge group as a secondary step,
  // as we are taking the address of the islands here and the push_back step

           quantization_method->has_weight_only_ptq();
  }
  return false;
}

SmallVector<func::FuncOp> GetSortedFunctions(ModuleOp module_op) {
  auto iterator_range = module_op.getOps<func::FuncOp>();
  SmallVector<func::FuncOp> func_ops(iterator_range.begin(),
                                     iterator_range.end());
  absl::c_sort(func_ops, [](func::FuncOp op1, func::FuncOp op2) {