Block &nested_block = nested_module->getRegion(0).front();
  for (func::FuncOp func_op :
       llvm::make_early_inc_range(nested_block.getOps<func::FuncOp>())) {
    if (!symbol_table.lookupSymbolIn(getOperation(), func_op.getName())) {
      nested_block.getOperations().remove(func_op.getOperation());
      symbol_table.insert(func_op.getOperation());
    }
  }
  nested_module->erase();
}

}  // namespace

    return "Add remarks based on cost analysis for testing purpose.";
  }
  void runOnOperation() override {
    const auto& cost_analysis = getAnalysis<CostAnalysis>();

    auto func_op = getOperation();
    for (auto& op : func_op.front()) {
      op.emitRemark() << "Cost: " << cost_analysis.GetCost(&op);
    }
  }
};

static mlir::PassRegistration<TestCostAnalysis> pass;

}  // namespace tfrt_compiler

  void runOnOperation() override;
};

void CanonicalizeCompileAndReplicateAttributesPass::runOnOperation() {
  func::FuncOp func_op = getOperation();
  ModuleOp module_op = func_op->getParentOfType<ModuleOp>();
  mlir::OpBuilder builder(module_op.getContext());

  auto walk_result = func_op->walk([&](mlir::Operation* op) {
    // Convert `_tpu_replicate`.
    if (op->hasAttr(TF::kTpuReplicateAttr)) {

// Take the following example:
//
// call @func_1_CPU {tac.interface_name = "func_1"}
//
// "func_1" is the interface name where "func_1_cpu" is the real implementation
// we can have multiple FuncOps like "func_1_cpu" and "func_1_gpu" and they
// both implement "func_1".
//
// The attribute on the FuncOp means what it actually implements while the

      kTfSavedModelIndexPathAttr, builder.getArrayAttr({file_prefix_attr}))});

  const int insert_idx = func_op.getNumArguments();

  func_op.insertArgument(insert_idx, /*argType=*/filename_op_type, arg_attrs,
                         NameLoc::get(file_prefix_attr));

  return func_op.getArgument(insert_idx);
}

// Creates a 1D string array constant for "tensor_names" input of `RestoreV2`

using ::tensorflow::kDefaultServingSignatureDefKey;
using ::tensorflow::kImportModelDefaultGraphFuncName;

// Returns true iff the function's symbol is public.
bool IsPublicFuncOp(func::FuncOp func_op) {
  return SymbolTable::getSymbolVisibility(&*func_op) ==
         SymbolTable::Visibility::Public;
}

}  // namespace

func::FuncOp FindMainFuncOp(ModuleOp module_op) {
  if (const auto main_func_op = module_op.lookupSymbol<func::FuncOp>(

 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() {

  OpPassManager &func_pm = pm.nest<func::FuncOp>();

  // First operates on the executor dialect:
  // - remove dead islands.
  // - fuse islands as much as possible.
  // - materialize the eventual "pass-through" ops by inlining their content.
  func_pm.addPass(tf_executor::CreateTFExecutorGraphPruningPass());
  func_pm.addPass(tf_executor::CreateTFExecutorIslandCoarseningPass());

 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 =

  explicit TensorArraySideEffectAnalysis(mlir::ModuleOp module);

  // Return if the function contains only non-side-effecting ops or TensorArray
  // ops.
  bool HasAtMostTensorArrayEffect(mlir::func::FuncOp func_op) const {
    return set_.count(func_op) > 0;
  }

 private:
  llvm::DenseSet<mlir::func::FuncOp> set_;
};

}  // namespace tfrt_compiler
}  // namespace tensorflow