void CountOp(DenseMap<StringRef, llvm::StringMap<int64_t>> &op_count_map,
               StringRef op_name, StringRef dtype);

 private:
  llvm::StringMap<int64_t> op_with_dialect_count_;
  DenseMap<StringRef, llvm::StringMap<int64_t>> op_dtype_count_;
  llvm::StringMap<int64_t> dialect_count_;
  llvm::StringMap<StringRef> dialect_name_of_;
  llvm::StringMap<StringRef> op_name_of_;

  DenseSet<Region*> discovered_;
  // Identifiers about mutable variables.
  // All variables are identified by (device, container, shared_name).
  DenseSet<std::tuple<llvm::StringRef, llvm::StringRef, llvm::StringRef>>
      mutable_variables_;
};

}  // namespace TF
}  // namespace mlir

#include <string>
#include <vector>

#include "llvm/ADT/StringRef.h"

namespace mlir {
namespace odml {

std::vector<std::string> GetAcceptedStableHLODialects();

std::vector<std::string> GetAcceptedTFLiteDialects();

// Can we find the given `dialect_name` in the `accepted_dialects`?
bool IsAcceptedDialect(llvm::StringRef dialect_name,
                       const std::vector<std::string> &accepted_dialects);

// -----

// CHECK-LABEL: func @testNoClusterFuncOpPasses
func.func @testNoClusterFuncOpPasses(%arg0: tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string> {
  %0 = "tf.Identity"(%arg0) : (tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string>
  func.return %0 : tensor<4x2x!tf_type.string>
}

// -----

func.func @_func(%arg0: tensor<i32>) -> tensor<i32> {

}

// Returns ID for identifying a resource.
std::tuple<StringRef, StringRef, StringRef> GetResourceKey(Operation* op) {
  StringRef device;
  if (auto attr = op->getAttrOfType<StringAttr>("device")) {
    device = attr.getValue();
  }

  StringRef container;
  if (auto attr = op->getAttrOfType<StringAttr>("container")) {
    container = attr.getValue();
  }

  StringRef shared_name;

struct TFRTSavedModelSignatureInfo {
  llvm::StringRef func_name;

  // The following are metadata for inputs.
  llvm::ArrayRef<llvm::StringRef> input_names;
  llvm::ArrayRef<
      std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
      input_specs;
  llvm::ArrayRef<llvm::StringRef> input_devices;

  // The following are metadata for outputs.
  llvm::ArrayRef<llvm::StringRef> output_names;
  llvm::ArrayRef<

inline constexpr StringRef kFusedFunctionAttr = "tf_quant.composite_function";
// The keyword to detect if this is a `NullAttribute`.
inline constexpr StringRef kNullAttributeValue = "N/A";

// Prefixes attached to lifted functions.
constexpr StringRef kQuantizedFuncPrefix = "quantized_";
constexpr StringRef kCompositeFuncPrefix = "composite_";

// `tf_device.cluster_func` ops.
// Output: A MLIR module in the Tensorflow Executor Dialect.
tensorflow::Status ExportFromTensorflowDialectToExecutor(
    mlir::ModuleOp module, llvm::StringRef module_name = llvm::StringRef());

}  // namespace v2
}  // namespace tf2xla
}  // namespace tensorflow

#include "absl/base/attributes.h"
#include "llvm/ADT/StringRef.h"
#include "mlir/Pass/PassManager.h"  // from @llvm-project

namespace tensorflow {
namespace tf2xla {
namespace internal {

// Given the pass manager, add Bridge passes to cluster the replicated input
// graphs.
void AddReplicatedBridgeClusteringPipelinePasses(
    mlir::OpPassManager& pm, llvm::StringRef module_name = llvm::StringRef());

    quantization_method_ = other.quantization_method_;
    op_set_ = other.op_set_;
  }

  StringRef getArgument() const final {
    // This is the argument used to refer to the pass in the textual format (on
    // the commandline for example).
    return "quant-insert-quantized-functions";
  }

  StringRef getDescription() const final {
    // This is a brief description of the pass.