code: def test_meter_update_from_report(self):
      meter = Meter()
      prepared_batch = SampleList(
          {"targets": torch.tensor([1, 2, 3, 4]), "dataset_type": "val"}
      )
      for idx in range(5):
          model_output = {
              "scores": torch.tensor([0, 1, 2, 3]),
              "losses": {"loss": float(idx)},
          }
          report = Report(prepared_batch, model_output)
          meter.update_from_report(report)
  
      self.assertEqual(meter.loss.global_avg, 2.0)
      self.assertEqual(meter.loss.avg, 2.0)
  
imports: import unittest
  import torch
  from mmf.common.meter import Meter
  from mmf.common.report import Report
  from mmf.common.sample import SampleList
label_desc: Use image node_id 1 for calling the TestMeter obj's underlying member method code with example usage: obj.test_meter_update_from_report() without return types