docstr: This test verifies that the meter works as expected on a single
  update + reset + same single update.
docstr_tok: ["This","test","verifies","that","the","meter","works","as","expected","on","a","single","update","+","reset","+","same","single","update","."]
code: def test_single_meter_update_and_reset(self):
      """
      This test verifies that the meter works as expected on a single
      update + reset + same single update.
      """
      meter = PrecisionAtKMeter(topk=[1, 2])
  
      # Batchsize = 3, num classes = 3, score is probability of class
      model_output = torch.tensor(
          [
              [0.2, 0.4, 0.4],  # top-1: 1/2, top-2: 1/2
              [0.2, 0.65, 0.15],  # top-1: 1, top-2: 1/0
              [0.33, 0.33, 0.34],  # top-1: 2, top-2: 2/0?1
          ]
      )
  
      # One-hot encoding, 1 = positive for class
      # sample-1: 1, sample-2: 0, sample-3: 0,1,2
      target = torch.tensor([[0, 1, 0], [1, 0, 0], [1, 1, 1]])
  
      # Note for ties, we select randomly, so we should not use ambiguous ties
      expected_value = {"top_1": 2 / 3.0, "top_2": 4 / 6.0}
  
      self.meter_update_and_reset_test(
          meter, model_output, target, expected_value
      )
  
file_path: ClassyVision/test/meters_precision_meter_test.py
imports: import torch
  from classy_vision import meters
  from classy_vision.meters import PrecisionAtKMeter
  from test.generic.meter_test_utils import ClassificationMeterTest
label_desc: Use image node_id 2 for calling the TestPrecisionAtKMeter obj's underlying member method code with example usage: obj.test_single_meter_update_and_reset() without return types