code: def test_profile(self):
      handler = TestMetricsHandler()
      stream = MetricStream("torchelastic", handler)
      # patch instead of configure to avoid conflicts when running tests in parallel
      with mock.patch(
          "torch.distributed.elastic.metrics.api.getStream",
          return_value=stream,
      ):
          self.bar()
  
          self.assertEqual(
              1, handler.metric_data["MetricsApiTest.bar.success"].value
          )
          self.assertNotIn(
              "MetricsApiTest.bar.failure", handler.metric_data
          )
          self.assertIn(
              "MetricsApiTest.bar.duration.ms", handler.metric_data
          )
  
          with self.assertRaises(RuntimeError):
              self.throw()
  
          self.assertEqual(
              1,
              handler.metric_data["MetricsApiTest.throw.failure"].value,
          )
          self.assertNotIn(
              "MetricsApiTest.bar_raise.success", handler.metric_data
          )
          self.assertIn(
              "MetricsApiTest.throw.duration.ms", handler.metric_data
          )
  
          self.bar2()
          self.assertEqual(
              "torchelastic",
              handler.metric_data[
                  "MetricsApiTest.bar2.success"
              ].group_name,
          )
  
imports: import abc
  import unittest.mock
  from torch.distributed.elastic.metrics.api import MetricData, MetricHandler, MetricStream, _get_metric_name, prof
  from torch.testing._internal.common_utils import run_tests, TestCase
label_desc: Use image node_id 6 for calling the MetricsApiTest obj's underlying member method code with example usage: obj.test_profile() without return types