public:
  virtual ~DeathTestFactory() { }
  virtual bool Create(const char* statement, const RE* regex,
                      const char* file, int line, DeathTest** test) = 0;
};

// A concrete DeathTestFactory implementation for normal use.
class DefaultDeathTestFactory : public DeathTestFactory {
 public:
  virtual bool Create(const char* statement, const RE* regex,
                      const char* file, int line, DeathTest** test);
};

    mutable scoped_ptr<const T> value_;
  };  // class ValuesInIteratorRangeGenerator::Iterator

  // No implementation - assignment is unsupported.
  void operator=(const ValuesInIteratorRangeGenerator& other);

  const ContainerType container_;
};  // class ValuesInIteratorRangeGenerator

// INTERNAL IMPLEMENTATION - DO NOT USE IN USER CODE.
//
// Stores a parameter value and later creates tests parameterized with that

#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/protobuf/saved_object_graph.pb.h"

namespace tensorflow {

// This is the TF eager runtime implementation of SignatureDefFunction (separate
// from the TFRT implementation). The user-facing API of SignatureDefFunctions
// and their semantic differences from ConcreteFunction are described here:

//
// A TensorHandle is management class around a Tensor which may track additional
// metadata and synchronization.
//
// This allows us to hide concrete implementations of TensorHandle from header
// files. The interface lists the common functionality that must be provided by
// any concrete implementation. However, in cases where the true concrete class
// is needed a static_cast can be applied.

#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope.h"

namespace tensorflow {
namespace ops {

/// GradFunc is the signature for all gradient functions in GradOpRegistry.
/// Implementations should add operations to compute the gradient outputs of
/// 'op' (returned in 'grad_outputs') using 'scope' and 'grad_inputs'.
typedef Status (*GradFunc)(const Scope& scope, const Operation& op,

  // running calibration.
  absl::StatusOr<ModuleOp> ImportCalibratedSavedModel(
      absl::string_view calibrated_saved_model_path);

  absl::Nonnull<MLIRContext*> ctx_;

  // Contains function implementations from the python layer. Should be injected
  // from the python level using pybind11.
  absl::Nonnull<const tensorflow::quantization::PyFunctionLibrary*>
      py_function_lib_;

  // Path to the pre-calibrated SavedModel.

#endif

typedef struct TF_Tensor TF_Tensor;

// --------------------------------------------------------------------------
// C API for TensorFlow Kernels.
//
// This API allows developers to register custom kernel implementations for
// TensorFlow.
//
// See c_api.h header comments for a discussion about API conventions.
//
// Users wishing to extend TensorFlow with new kernels will call

  //
  // Unlike wcscasecmp(), this function can handle NULL argument(s).
  // A NULL C string is considered different to any non-NULL wide C string,
  // including the empty string.
  // NB: The implementations on different platforms slightly differ.
  // On windows, this method uses _wcsicmp which compares according to LC_CTYPE
  // environment variable. On GNU platform this method uses wcscasecmp

// for more examples.
//
// In the future, we plan to publish the API for defining new parameter
// generators. But for now this interface remains part of the internal
// implementation and is subject to change.
//
//
// A parameterized test fixture must be derived from testing::Test and from
// testing::WithParamInterface<T>, where T is the type of the parameter

Type ShuffleRankedTensorType(Type type, ArrayRef<int64_t> permutation);

bool AreCancellablePermutations(DenseIntElementsAttr perm0,
                                DenseIntElementsAttr perm1);

// Default implementation of `LayoutSensitiveInterface::UpdateDataFormat` for
// layout sensitive operations that do not have any additional layout dependent
// attributes besides `data_format` string.
template <typename Op>