code: def test_meter_distributed(self):
      # Meter0 will execute on one process, Meter1 on the other
      meters = [
          PrecisionAtKMeter(topk=[1, 2]),
          PrecisionAtKMeter(topk=[1, 2]),
      ]
  
      # Batchsize = 3, num classes = 3, score is probability of class
      model_outputs = [
          torch.tensor(
              [[0.3, 0.4, 0.3], [0.2, 0.65, 0.15], [0.33, 0.33, 0.34]]
          ),  # Meter 0
          torch.tensor(
              [[0.05, 0.4, 0.05], [0.15, 0.65, 0.2], [0.4, 0.2, 0.4]]
          ),  # Meter 1
          torch.tensor(
              [[0.3, 0.4, 0.3], [0.2, 0.65, 0.15], [0.33, 0.33, 0.34]]
          ),  # Meter 0
          torch.tensor(
              [[0.05, 0.4, 0.05], [0.15, 0.65, 0.2], [0.4, 0.2, 0.4]]
          ),  # Meter 1
      ]
  
      # Class 0 is the correct class for sample 1, class 2 for sample 2, etc
      targets = [
          torch.tensor([[0, 1, 0], [1, 0, 0], [1, 1, 1]]),  # Meter 0
          torch.tensor([[0, 1, 0], [0, 1, 0], [0, 1, 0]]),  # Meter 1
          torch.tensor([[0, 1, 0], [1, 0, 0], [1, 1, 1]]),  # Meter 0
          torch.tensor([[0, 1, 0], [0, 1, 0], [0, 1, 0]]),  # Meter 1
      ]
  
      # In first two updates there are 4 correct top-1, 6 correct in top 2
      # The same occurs in the second two updates and is added to first
      expected_values = [
          {
              "top_1": 4 / 6.0,
              "top_2": 6 / 12.0,
          },  # After one update to each meter
          {
              "top_1": 8 / 12.0,
              "top_2": 12 / 24.0,
          },  # After two updates to each meter
      ]
  
      self.meter_distributed_test(
          meters, model_outputs, targets, expected_values
      )
  
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 8 for calling the TestPrecisionAtKMeter obj's underlying member method code with example usage: obj.test_meter_distributed() without return types