code: def test_smoke(self, device, dtype):
      boxes = torch.tensor(
          [[100, 50, 150, 100.0]], device=device, dtype=dtype
      )
      labels = torch.tensor([1], device=device, dtype=torch.long)
      scores = torch.tensor([0.7], device=device, dtype=dtype)
  
      gt_boxes = torch.tensor(
          [[100, 50, 150, 100.0]], device=device, dtype=dtype
      )
      gt_labels = torch.tensor([1], device=device, dtype=torch.long)
  
      mean_ap = kornia.metrics.mean_average_precision(
          [boxes], [labels], [scores], [gt_boxes], [gt_labels], 2
      )
  
      assert_close(
          mean_ap[0], torch.tensor(1.0, device=device, dtype=dtype)
      )
      assert_close(mean_ap[1][1], 1.0)
  
imports: import pytest
  import torch
  import kornia
  from kornia.testing import BaseTester, assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 1 for calling the TestMeanAveragePrecision obj's underlying member method code with example usage: obj.test_smoke(device, dtype) without return types