code: def test_yaml_then_api(self, caplog):
      metrics_cache_obj = MetricsCacheYamlImpl(
          os.path.join(dir_path, "metrics_api.yaml")
      )
      metrics_cache_obj.initialize_cache()
      metrics_cache_obj.add_metric_to_cache(
          "InferenceTimeInMS", "ms", ["ModelName"]
      )
      metrics_cache_obj.add_metric_to_cache(
          "GaugeModelMetricNameExample",
          "MB",
          ["Level"],
          MetricTypes.GAUGE,
      )
      metrics_cache_obj.add_metric_to_cache("LoopCountError", "")
      print(metrics_cache_obj.cache_keys())
      assert len(metrics_cache_obj.cache_keys()) == 5
      counter_metric = metrics_cache_obj.get_metric(
          "InferenceTimeInMS", MetricTypes.COUNTER
      )
      gauge_metric = metrics_cache_obj.get_metric(
          "GaugeModelMetricNameExample", MetricTypes.GAUGE
      )
      error_metric = metrics_cache_obj.get_metric(
          "LoopCountError", MetricTypes.COUNTER
      )
      caplog.set_level("INFO")
      counter_metric.add_or_update(2.7, ["test_yaml_then_api"])
      gauge_metric.add_or_update(25.17, ["model"])
      error_metric.add_or_update(5)
      assert (
          "InferenceTimeInMS.Milliseconds:2.7|#ModelName:test_yaml_then_api|"
          in caplog.text
      )
      assert (
          "GaugeModelMetricNameExample.Megabytes:25.17|#Level:model|"
          in caplog.text
      )
      assert "LoopCountError.unit:5|#|" in caplog.text
  
imports: import os
  import uuid
  import pytest
  import ts.metrics.metric_cache_errors
  from ts.metrics.caching_metric import CachingMetric
  from ts.metrics.dimension import Dimension
  from ts.metrics.metric_cache_yaml_impl import MetricsCacheYamlImpl
  from ts.metrics.metric_type_enum import MetricTypes
label_desc: Use image node_id 1 for calling the TestAPIAndYamlParse obj's underlying member method code with example usage: obj.test_yaml_then_api(caplog) without return types