key = new Object();
    cache.refresh(key);
    checkNothingLogged();
    stats = cache.stats();
    assertThat(stats.missCount()).isEqualTo(2);
    assertThat(stats.loadSuccessCount()).isEqualTo(3);
    assertThat(stats.loadExceptionCount()).isEqualTo(0);
    assertThat(stats.hitCount()).isEqualTo(0);

    assertThat(cache.get(key)).isSameInstanceAs(key);
    stats = cache.stats();

  TF_DeleteStatus(status);

  return var_handle;
}

TFE_Op* AddOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b) {
  TF_Status* status = TF_NewStatus();

  TFE_Op* op = TFE_NewOp(ctx, "AddV2", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, a, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, b, status);

    TFE_TensorHandle* h = TFE_NewTensorHandle(t, status);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    TF_DeleteTensor(t);

    TFE_Op* op = TFE_NewOp(ctx, "ident", status);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    TFE_OpAddInput(op, h, status);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);

    std::vector<TFE_TensorHandle*> result;

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build an abstract operation.
  auto* add_op = TF_NewAbstractOp(graph_ctx);
  TF_AbstractOpSetOpType(add_op, "Add", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_AbstractOpSetOpName(add_op, "my_add", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpAddInput(op, var, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  int num_retvals = 1;
  TFE_Execute(op, out_value, &num_retvals, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteOp(op);
  TF_DeleteStatus(status);
}

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());

  status->status = c.construction_status();
  if (!status->status.ok()) return;

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);

  virtual absl::Status SetDeviceName(const char* name) = 0;

  virtual absl::Status AddInput(AbstractTensorHandle* input) = 0;
  virtual absl::Status AddInputList(
      absl::Span<AbstractTensorHandle* const> inputs) = 0;
  virtual absl::Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) = 0;

        response.jobLogId("test-id").status(Status.OK);
        assertEquals("test-id", response.jobLogId);
        assertEquals(0, response.status);
    }

    @Test
    public void test_ApiStartJobResponse_result() {
        final ApiStartJobResponse response = new ApiStartJobResponse();
        response.jobLogId("log-id-xyz").status(Status.OK);
        final ApiResult result = response.result();

    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_OpAddInput(matmul, m, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_OpAddInput(matmul, m, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_Execute(matmul, &retvals[0], &num_retvals, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);