code: def test_meter_distributed(self):
      # Meter0 will execute on one process, Meter1 on the other
      meters = [AccuracyMeter(topk=[1, 2]), AccuracyMeter(topk=[1, 2])]
  
      # Batchsize = 3, num classes = 3, score is a value in {1, 2,
      # 3}...3 is the highest score
      model_outputs = [
          torch.tensor([[3, 2, 1], [3, 1, 2], [1, 3, 2]]),  # Meter 0
          torch.tensor([[3, 2, 1], [1, 3, 2], [1, 3, 2]]),  # Meter 1
          torch.tensor([[3, 2, 1], [3, 1, 2], [1, 3, 2]]),  # Meter 0
          torch.tensor([[3, 2, 1], [1, 3, 2], [1, 3, 2]]),  # Meter 1
      ]
  
      # Class 0 is the correct class for sample 1, class 2 for sample 2, etc
      targets = [
          torch.tensor([0, 1, 2]),  # Meter 0
          torch.tensor([0, 1, 2]),  # Meter 1
          torch.tensor([0, 1, 2]),  # Meter 0
          torch.tensor([0, 1, 2]),  # Meter 1
      ]
  
      # In first two updates there are 3 correct top-2, 5 correct in top 2
      # The same occurs in the second two updates and is added to first
      expected_values = [
          {
              "top_1": 3 / 6.0,
              "top_2": 5 / 6.0,
          },  # After one update to each meter
          {
              "top_1": 6 / 12.0,
              "top_2": 10 / 12.0,
          },  # After two updates to each meter
      ]
  
      self.meter_distributed_test(
          meters, model_outputs, targets, expected_values
      )
  
file_path: ClassyVision/test/meters_accuracy_meter_test.py
imports: import torch
  from classy_vision import meters
  from classy_vision.meters import AccuracyMeter
  from test.generic.meter_test_utils import ClassificationMeterTest
label_desc: Use image node_id 10 for calling the TestAccuracyMeter obj's underlying member method code with example usage: obj.test_meter_distributed() without return types