code: def test_meter_get_set_classy_state_test(self):
      # In this test we update meter0 with model_output0 & target0
      # and we update meter1 with model_output1 & target1 then
      # transfer the state from meter1 to meter0 and validate they
      # give same expected value.
      #
      # Expected value is the expected value of meter1 For this test
      # to work, top-1 / top-2 values of meter0 / meter1 should be
      # different
      meters = [
          PrecisionAtKMeter(topk=[1, 2]),
          PrecisionAtKMeter(topk=[1, 2]),
      ]
      model_outputs = [
          torch.tensor(
              [[0.05, 0.4, 0.05], [0.2, 0.65, 0.15], [0.33, 0.33, 0.34]]
          ),
          torch.tensor(
              [[0.05, 0.4, 0.05], [0.15, 0.65, 0.2], [0.4, 0.2, 0.4]]
          ),
      ]
      targets = [
          torch.tensor([[0, 1, 0], [1, 0, 0], [1, 1, 0]]),
          torch.tensor([[0, 1, 0], [0, 1, 0], [0, 1, 0]]),
      ]
  
      # Second update's expected value
      expected_value = {"top_1": 2 / 3.0, "top_2": 2 / 6.0}
  
      self.meter_get_set_classy_state_test(
          meters, model_outputs, targets, 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 7 for calling the TestPrecisionAtKMeter obj's underlying member method code with example usage: obj.test_meter_get_set_classy_state_test() without return types