return s
	}
	getExpectedEndpoints := func(args []string, prefix string) ([]*url.URL, []bool) {
		var URLs []*url.URL
		var localFlags []bool
		for _, arg := range args {
			u, _ := url.Parse(arg)
			URLs = append(URLs, u)
			localFlags = append(localFlags, strings.HasPrefix(arg, prefix))
		}

		return URLs, localFlags
	}

	case1Endpoint1 := "http://" + nonLoopBackIP + "/d1"

                                                  TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<tensorflow::OpKernelContext*>(ctx);
  const tensorflow::Tensor& arg_tensor = cc_ctx->input(index);
  absl::Status cc_status;
  if (arg_tensor.dtype() != tensorflow::DT_RESOURCE) {
    cc_status = absl::InvalidArgumentError(
        absl::StrCat("Trying to obtain resource handle from Input[", index,

  // First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_OperationDescription* id_descr =

  static <C, E extends @Nullable Object, R extends Iterable<E>>
      BiPredicate<C, C> pairwiseOnResultOf(Function<C, R> arg) {
    Equivalence<C> equivalence = Equivalence.equals().<E>pairwise().onResultOf(arg);
    return equivalence::equivalent;
  }

         */
        public int add2(final int arg1, final int arg2) {
            return arg1 + arg2;
        }

        /**
         * @param arg
         * @return Integer
         */
        public Integer echo(final Integer arg) {
            return arg;
        }

        /**
         *
         */
        public void throwException() {
            throw new IllegalStateException("hoge");

  CHECK(tensorflow::protobuf::TextFormat::ParseFromString(
      "    signature {"
      "      name: 'AddVariablesFunction'"
      "      input_arg {"
      "        name: 'var'"
      "        type: DT_RESOURCE"
      "      }"
      "      output_arg {"
      "        name: 'sum'"
      "        type: DT_FLOAT"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read0'"

        mode: KtConstantEvaluationMode,
    ): FirLiteralExpression<String>? {
        val concatenated = buildString {
            for (arg in stringConcatenationCall.arguments) {
                val evaluated = evaluate(arg, mode) ?: return null
                append(evaluated.value.toString())
            }
        }

	// currently isn't supported for asm functions.
	if nameAddr.Sym.ABI() == obj.ABIInternal && flag&obj.NOSPLIT == 0 {
		p.errorf("TEXT %q: ABIInternal requires NOSPLIT", name)
	}

	// Next operand is the frame and arg size.
	// Bizarre syntax: $frameSize-argSize is two words, not subtraction.
	// Both frameSize and argSize must be simple integers; only frameSize
	// can be negative.

  // Tensor creation functions
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, absl::Span<const int64_t> dim_sizes) = 0;

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.

static void BoolDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<bool*>(data);
}

static void Int32Deallocator(void* data, size_t, void* arg) {
  delete[] static_cast<int32_t*>(data);
}

static void DoubleDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<double*>(data);
}

static void FloatDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<float*>(data);
}