stats = append(stats, madmin.KMS{
			Status:   string(state),
			Endpoint: endpoint,
		})
	}
	return stats
}

func targetStatus(ctx context.Context, h logger.Target) madmin.Status {
	if h.IsOnline(ctx) {
		return madmin.Status{Status: string(madmin.ItemOnline)}
	}
	return madmin.Status{Status: string(madmin.ItemOffline)}
}

// fetchLoggerInfo return log info

		return nil, toAdminAPIErr(h.ctx, err), ""
	}
	return b, noError, ""
}

// PopHealStatusJSON - Called by heal-status API. It fetches the heal
// status results from global state and returns its JSON
// representation. The clientToken helps ensure there aren't
// conflicting clients fetching status.
func (ahs *allHealState) PopHealStatusJSON(hpath string,
	clientToken string) ([]byte, APIErrorCode,
) {

  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());

### Feature

- Add paths section to kubelet statusz endpoint ([#133239](https://github.com/kubernetes/kubernetes/pull/133239), [@Peac36](https://github.com/Peac36)) [SIG Node]

  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();