op->removeAttr(kTPUEmbeddingAttr);
  return success();
}

LogicalResult FindTPUEmbeddingOps(
    func::FuncOp func_op, llvm::StringMap<Operation*>* enqueue_op_map,
    llvm::StringMap<Operation*>* recv_activation_op_map,
    llvm::StringMap<Operation*>* send_gradient_op_map) {
  auto walk_result = func_op.walk([&](Operation* op) {
    if (llvm::isa<TF::RecvTPUEmbeddingActivationsOp>(op))

  // we do not overwrite the function argument mapping here.
  func_op.walk([&](Operation* op) {
    if (op == func_op) return;
    for (Region& region : op->getRegions()) {
      assign_unique_id_to_all(region.getArguments());
    }
  });

  llvm::SmallDenseMap<ResourceHandle, int64_t> resource_handle_id_map;
  func_op.walk([&](Operation* op) {

 public:
  void runOnOperation() override;
};

void InferenceMetricsPass::runOnOperation() {
  bool has_tpu_partitioned_call = false;
  ModuleOp module = getOperation();

  for (auto func_op : module.getOps<func::FuncOp>()) {
    func_op->walk(
        [&](TF::TPUPartitionedCallOp op) { has_tpu_partitioned_call = true; });

    if (has_tpu_partitioned_call) break;
  }

  std::string has_tpu_partitioned_call_str =

 public:
  // void runOnOperation() override;
  void runOnOperation() override {
    // Strip the "tf._noinline" attribute from top-level functions.
    for (auto func_op : getOperation().getOps<func::FuncOp>())
      func_op->removeAttr("tf._noinline");
  }
};

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>> CreateStripNoinlineAttributePass() {

  func::FuncOp func_op = getOperation();
  MLIRContext* context = func_op.getContext();
  RewritePatternSet patterns(context);

  BFloat16TypeConverter converter;
  patterns.add<BFloat16TypePattern, BitcastConvertOpPattern>(converter,
                                                             context);
  populateFunctionOpInterfaceTypeConversionPattern<func::FuncOp>(patterns,

      component.Run(*module_op, quantization_config);

  EXPECT_THAT(pre_calibration_result, IsOk());

  SmallVector<func::FuncOp> func_ops;
  for (auto func_op : pre_calibration_result->getOps<func::FuncOp>()) {
    func_ops.push_back(func_op);
  }
  ASSERT_THAT(func_ops, SizeIs(2));
  EXPECT_THAT(func_ops, Contains(HasSymName("main")));

// Adds a new input name to the `inputs` field of the `tf.entry_function`
// attribute if the attribute exist in the given function. Otherwise, no
// attribute is modified.
void AddEntryFunctionInput(StringRef input_name, func::FuncOp func_op);

}  // namespace quant
}  // namespace mlir

    return {OperandIdx...};
  }
};

// Finds all op of type `T::OpType` `func_op` and recursively duplicates
// constants used at the op's operands at `T::OperandIndices()`. It sequentially
// does the same thing for `Ts`.
template <typename T, typename... Ts>
void DuplicateShapeDeterminingConstants(func::FuncOp func_op) {
  for (auto op : func_op.getOps<typename T::OpType>()) {
    RecursivelyDuplicateConstantsForOperands(

    SymbolTable symbol_table(call_op->getParentOfType<ModuleOp>());
    auto func_op =
        dyn_cast_or_null<func::FuncOp>(symbol_table.lookup(func_name));
    if (!func_op) return failure();
    // The quantized fusion should have requantize and return ops at the end.
    auto return_op = dyn_cast_or_null<func::ReturnOp>(
        func_op.getRegion().getBlocks().front().getTerminator());
    if (!return_op) return failure();

    : public impl::VerifyClusteringPassBase<VerifyClusteringPass> {
 public:
  void runOnOperation() override;
};

void VerifyClusteringPass::runOnOperation() {
  Operation* func_op = getOperation();

  auto walk_result = func_op->walk([&](Operation* op) {
    if (!tensorflow::tf2xla::internal::IsInBridgeAcceptableDialects(op)) {
      std::string error = "op is in dialect " +