*=================================================================*/

CU_EXPORT 
CU_ErrorCode CU_initialize_registry(void);
/**<
 *  Initializes the framework test registry.
 *  Any existing registry is freed, including all stored suites 
 *  and associated tests.  It is not necessary to explicitly call
 *  CU_cleanup_registry() before reinitializing the framework.
 *  The most recent stored test results are also cleared.<br /><br />
 *

// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,

// `tensorflow::mutex_lock` here but we have ops (`tf.MutexLock` and
// `tf.ConsumeMutexLock`) with getter methods named as `mutex_lock()`. Need to
// undefine here to avoid expanding the getter symbol as macro when including
// both mutex.h and this header file.
#undef mutex_lock

#define GET_OP_FWD_DEFINES
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_all_ops.h.inc"
#define GET_OP_CLASSES

  explicit AbstractContext(AbstractContextKind kind) : kind_(kind) {}
  virtual ~AbstractContext() {}

 public:
  AbstractContextKind getKind() const { return kind_; }

  // Release any underlying resources, including the interface object.
  //
  // WARNING: The destructor of this class is marked as protected to disallow
  // clients from directly destroying this object since it may manage its own

#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tensorflow/lite/toco/types.pb.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {
namespace internal {

// Register all custom ops including user specified custom ops.
Status RegisterAllCustomOps(const toco::TocoFlags& toco_flags);

// Populate quantization specs (or not) given user specified ranges for each
// input arrays.
Status PopulateQuantizationSpecs(

  StringRef getName() final { return "_XlaRun"; }
};

// Returns true iff resource type with given ID is only self-dependent, i.e.,
// there are no dependencies to other resource types (including unknown resource
// type).
inline bool IsOnlySelfDependent(TypeID resource_type_id) {
  return resource_type_id == ResourceEffects::Send::getResourceID() ||

// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,

// attached to a `func::FuncOp`.
void UpdateFunctionAliases(
    absl::flat_hash_map<FunctionName, FunctionAlias>& function_aliases,
    ModuleOp module_op);

// Loads a SavedModel to `mlir::ModuleOp` and performs preprocesses including
// shape inference and graph freezing.
// TODO: b/329206105 - Add unit tests after decomposing preprocessing passes.
absl::StatusOr<OwningOpRef<ModuleOp>> ImportSavedModel(
    absl::string_view saved_model_path,

// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,

  Status AllocateShapedBuffer(DataType dtype, const xla::Shape& on_device_shape,
                              xla::LocalClient* client, int device_ordinal);

  // Some Tensors can have complex on-device shapes, including tuple shapes. To
  // manage the memory for these tensors a ShapedBuffer may be required.

  // Return true if this XlaTensor contains a ShapedBuffer.
  bool has_shaped_buffer() const { return shaped_buffer_.has_value(); }