template <typename G>
  Cleanup(Cleanup<G>&& src)  // NOLINT
      : released_(src.is_released()), f_(src.release()) {}

  // Assignment to a Cleanup object behaves like destroying it
  // and making a new one in its place, analogous to unique_ptr
  // semantics.
  Cleanup& operator=(Cleanup&& src) {  // NOLINT
    if (!released_) f_();
    released_ = src.released_;

// Destroy an options object.
TF_CAPI_EXPORT extern void TFE_DeleteContextOptions(TFE_ContextOptions*);

// "Context" under which operations/functions are executed. It encapsulates
// things like the available devices, resource manager etc.
// TFE_Context must outlive all tensor handles created using it. In other
// words, TFE_DeleteContext() must be called after all tensor handles have

extern "C" {
#endif

// Test only exports of the monitoring Cell Reader API which allows tests to
// read current values from streamz counters defined in other modules.
//
// The code under test will have created streamz counters like this:
// auto* streamz = tensorflow::monitoring::Counter<1>::New("name",
// "description", "label");
// and then incremented that counter for various values of label:
// streamz->GetCell("label-value")->IncrementBy(1);
//

//  * TF_NOT_FOUND - dirname doesn't exist
//  * TF_PERMISSION_DENIED - dirname or some descendant is not writable
//  * TF_UNIMPLEMENTED - some underlying functions (like Delete) are not
//    implemented
TF_CAPI_EXPORT extern void TF_DeleteRecursively(const char* dirname,
                                                int64_t* undeleted_file_count,

// * Backing store for tensor memory has an associated deallocation
//   function.  This deallocation function will point to client code
//   for tensors populated by the client.  So the client can do things
//   like shadowing a numpy array.
// * We do not provide TF_OK since it is not strictly necessary and we
//   are not optimizing for convenience.
// * We make assumption that one session has one graph.  This should be

          if (op_it->second.output_tensor_info[j].GetID() == id) {
            found = true;
            Gradient* ones_like = nullptr;
            TF_RETURN_IF_ERROR(vspace.BuildOnesLike(
                op_it->second.output_tensor_info[j], &ones_like));
            (*result)[id].push_back(ones_like);
            break;
          }
        }
        if (!found) {
          return errors::Internal(

// It's going to be hard to include a whole real JVM to test this.
// So we'll simulate a really easy JVM using just the parts we need.

// This is the relevant part of jni.h.

// On Android NDK16, jobject is defined like this in C and C++
typedef void* jobject;

typedef jobject jclass;
typedef jobject jthrowable;
typedef jobject jstring;
typedef jobject jarray;
typedef jarray jbooleanArray;
typedef jarray jbyteArray;

// TF_NewAttrBuilder() returns an object that you can set attributes on as
// though it were an op. This allows querying properties of that op for
// type-checking purposes like if the op will run on a particular device type.
typedef struct TF_AttrBuilder TF_AttrBuilder;
TF_CAPI_EXPORT extern TF_AttrBuilder* TF_NewAttrBuilder(const char* op_name);

// have been executed. Note that "execution" here refers to kernel execution /
// scheduling of copies, etc. Similar to sync execution, it doesn't guarantee
// that lower level device queues (like GPU streams) have been flushed.
//
// This call may not block for execution of ops enqueued concurrently with this
// call.
TF_CAPI_EXPORT extern void TFE_ExecutorWaitForAllPendingNodes(

///
/// Except where noted otherwise, all pointer arguments are owned by core
/// TensorFlow and are guaranteed to not be `nullptr`.
///
/// All path-like arguments are null terminated `char*` strings. Plugins can
/// assume that before any function using path arguments is invoked, the path is
/// made canonical by calling the function provided by `translate_name` or a