docstr: Runs a valid meter test. Does not reset meter before / after running
docstr_tok: ["Runs","a","valid","meter","test",".","Does","not","reset","meter","before","\/","after","running"]
code: def _apply_updates_and_test_meter(
      self, meter, model_output, target, expected_value, **kwargs
  ):
      """
      Runs a valid meter test. Does not reset meter before / after running
      """
      if not isinstance(model_output, list):
          model_output = [model_output]
  
      if not isinstance(target, list):
          target = [target]
  
      for i in range(len(model_output)):
          meter.update(model_output[i], target[i], **kwargs)
  
      meter.sync_state()
      meter_value = meter.value
      for key, val in expected_value.items():
          self.assertTrue(
              key in meter_value,
              msg="{0} not in meter value!".format(key),
          )
          if torch.is_tensor(meter_value[key]):
              self.assertTrue(
                  torch.all(torch.eq(meter_value[key], val)),
                  msg="{0} meter value mismatch!".format(key),
              )
          else:
              self.assertAlmostEqual(
                  meter_value[key],
                  val,
                  places=4,
                  msg="{0} meter value mismatch!".format(key),
              )
  
imports: import multiprocessing
  import queue
  import tempfile
  import unittest
  import torch
label_desc: Use image node_id 4 for calling the ClassificationMeterTest obj's underlying member method code with example usage: obj._apply_updates_and_test_meter(meter, model_output, target, expected_value) without return types