StrEq("quantized_my_function"));
  EXPECT_TRUE(first_result.method().has_no_quantization());
}

TEST_F(QuantizationReportTest, InitializeWithModuleOp) {
  constexpr absl::string_view kQuantizedDotGeneral = R"mlir(
    func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/lite/c/builtin_op_data.h"

namespace mlir {
namespace TFL {

namespace {

constexpr char kTFImplements[] = "tf._implements";
constexpr char kMaxUnpooling[] = "MaxUnpooling2D";
constexpr char kImageWarping[] = "DenseImageWarp";

inline ConstBytesAttr CustomOption(OpBuilder* builder,
                                   const std::string& content) {

// `tf.backend_config` is a DictionaryAttr, JAX2TF sets the value of its
// i64 attribute `called_index` to the TF function's name.
constexpr llvm::StringRef kTfBackendConfigAttrName = "tf.backend_config";
constexpr llvm::StringRef kCalledIndexAttrName = "called_index";
constexpr llvm::StringRef kCalledFuncAttrName = "called_func";

// Converts `called_func` attributes in custom call ops back to `called_index`.

  MLIRContext *context =
      (*op)->template getParentOfType<ModuleOp>().getContext();

  // We only support NHWC <-> NCHW permutations.
  static constexpr std::array<int64_t, 4> kNchwToNhwc = {0, 2, 3, 1};
  static constexpr std::array<int64_t, 4> kNhwcToNchw = {0, 3, 1, 2};

  // Operation data format after folding `permutation`.
  StringRef target_data_format = [&]() -> StringRef {

  const size_t count_us = sorted_us.size();
  double sum_us = 0;
  size_t count_us_trimmed = 0;
  double sum_us_trimmed = 0;
  size_t count_us_best = 0;
  double sum_us_best = 0;
  static constexpr float trim_ratio = 0.25;
  static constexpr float best_ratio = 0.1;
  const size_t count_trimmed = count_us * trim_ratio;
  const size_t count_best = count_us * best_ratio;
  for (size_t i = 0; i < sorted_us.size(); ++i) {

// `tf.backend_config` is a DictionaryAttr, JAX2TF sets the value of its
// i64 attribute `called_index` to the TF function's name.
constexpr llvm::StringRef kTfBackendConfigAttrName = "tf.backend_config";
constexpr llvm::StringRef kCalledIndexAttrName = "called_index";
constexpr llvm::StringRef kCalledFuncAttrName = "called_func";

// Deserialize the StableHLO module embedded in XlaCallModuleOp's module
// attribute.

namespace mlir {
namespace TFTPU {

constexpr char kStepMarkerLocationAttr[] = "step_marker_location";
constexpr char kDevicesAttr[] = "devices";
constexpr char kVersionsAttr[] = "tf.versions";
constexpr char kUseXlaSpmdAttr[] = "use_spmd_for_xla_partitioning";

constexpr char kBadStringArrayElementMsg[] =
    "bad '{0}' attribute at index {1}, not a string";
constexpr char kBadArrayElementMsg[] =

// remove the attribute and skip further processing on those ops.
constexpr char kNoFallbackAttr[] = "no_fallback";
// TF Quantization modes. These constants are defined as char arrays so they
// can parsed by the pass option.
constexpr char kDefaultMode[] = "DEFAULT";
constexpr char kLegacyIntegerMode[] = "LEGACY_INTEGER";

// Checks if the operation is TF FakeQuant ops.

#include "mlir/IR/Operation.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

inline constexpr absl::string_view kCrashReproducerStdErr = "-";
inline constexpr absl::string_view kCrashReproducerCrashAnalysis =
    "crash_analysis";

// Creates a file to use for dumping and returns success if a file could be

using mlir::TF::ReadVariableOp;
using mlir::tf_device::ReplicateOp;

#define GEN_PASS_DEF_HOISTBROADCASTREADPASS
#include "tensorflow/compiler/mlir/tf2xla/internal/passes/clustering_passes.h.inc"

constexpr char kFuncDeviceAttr[] = "tf.device";
constexpr char kCpuDeviceType[] = "CPU";

struct HoistBroadcastRead
    : public impl::HoistBroadcastReadPassBase<HoistBroadcastRead> {
  void runOnOperation() override;
};