return absl::OkStatus();
  }

  absl::Status RegisterFunction(AbstractFunction* func) override {
    return errors::Unimplemented(
        "Registering graph functions has not been implemented yet.");
  }

  absl::Status RemoveFunction(const string& func) override {
    return errors::Unimplemented(
        "GraphContext::RemoveFunction has not been implemented yet.");
  }
  // For LLVM style RTTI.

  return tensorflow::errors::Unimplemented(
      "SetAttrFunction has not been implemented yet.");
}
absl::Status SetAttrFunctionName(AbstractOperation* op_, const char* attr_name,
                                 const char* value, size_t length,
                                 ForwardOperation* forward_op_) {
  return tensorflow::errors::Unimplemented(
      "SetAttrFunctionName has not been implemented "

void TFE_DeleteContextOptions(TFE_ContextOptions* options) { delete options; }

TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) {
  if (opts->use_tfrt) {
    status->status = tensorflow::errors::Unimplemented("TFRT is not supported");
    return nullptr;
  }
  std::vector<std::unique_ptr<tensorflow::Device>> devices;
  status->status = tensorflow::DeviceFactory::AddDevices(

TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs, int ninputs,
                           TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "Creating while loops is not supported on mobile. File a bug at "
      "https://github.com/tensorflow/tensorflow/issues if this feature is "
      "important to you");
  return EmptyWhileParams();
#else

   * Java 8. If we include the {@code getAnnotatedBounds()} method then its return type means it
   * won't compile on Java 7, while if we don't include the method then the compiler will complain
   * that an abstract method is unimplemented. So instead we use a dynamic proxy to get an
   * implementation. If the method being called on the {@code TypeVariable} instance has the same

          combined_shape.dims() != component_shape.dims()) {
        PartialTensorShape first_shape;
        TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&first_shape));
        return errors::Unimplemented(absl::StrCat(
            "Computing the shape of a ParallelTensor when the components do "
            "not all have the same rank is not supported. One tensor had "
            "shape ",

TF_Library* TF_LoadPluggableDeviceLibrary(const char* library_filename,
                                          TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "PluggableDevice plugin functionality is not supported on mobile");
  return nullptr;
#else
  TF_Library* lib_handle = new TF_Library;
  static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED);

   * @return the converted instance; <b>must not</b> be null
   * @throws UnsupportedOperationException if backward conversion is not implemented; this should be
   *     very rare. Note that if backward conversion is not only unimplemented but
   *     unimplement<i>able</i> (for example, consider a {@code Converter<Chicken, ChickenNugget>}),
   *     then this is not logically a {@code Converter} at all, and should just implement {@link
   *     Function}.

  void (*delete_device)(void* device_info);

  // Implements TFE_CreatePackedTensorHandle when one of `handles` is on this
  // custom device.
  //
  // Many devices will want to simply return an "unimplemented" status
  // here. This is the default behavior if `pack` is null when passed to
  // TFE_RegisterCustomDevice.
  TFE_TensorHandle* (*pack)(TFE_Context* context, TFE_TensorHandle** handles,

		} else {
			r1, r2, scale, ok = p.register(tok.String(), 0)
			if !ok {
				p.errorf("indirect through non-register %s", tok)
			}
			if r2 != 0 {
				p.errorf("unimplemented two-register form")
			}
			a.Index = r1
			if scale != 0 && scale != 1 && (p.arch.Family == sys.ARM64 ||
				p.arch.Family == sys.PPC64) {
				// Support (R1)(R2) (no scaling) and (R1)(R2*1).