diff --git a/tests/gamma/csm_observability_test.py b/tests/gamma/csm_observability_test.py
index 9db3c089..24eb0d37 100644
--- a/tests/gamma/csm_observability_test.py
+++ b/tests/gamma/csm_observability_test.py
@@ -168,93 +168,6 @@ def initKubernetesServerRunner(self, **kwargs) -> GammaServerRunner:
             csm_canonical_service_name=CSM_CANONICAL_SERVICE_NAME_SERVER,
         )
 
-    def assertAtLeastOnePointWithinRange(
-        self,
-        points: list[monitoring_v3.types.Point],
-        ref_bytes: int,
-        tolerance: float = 0.05,
-    ):
-        """
-        A helper function to check whether at least one of the "points" whose
-        mean should be within X% of ref_bytes.
-        """
-        for point in points:
-            if (
-                ref_bytes * (1 - tolerance)
-                < point.value.distribution_value.mean
-                < ref_bytes * (1 + tolerance)
-            ):
-                return
-        self.fail(
-            f"No data point with {ref_bytes}±{tolerance*100}% bytes found"
-        )
-
-    @classmethod
-    def build_histogram_query(cls, metric_type: str) -> str:
-        #
-        # The list_time_series API requires us to query one metric
-        # at a time.
-        #
-        # The 'grpc_status = "OK"' filter condition is needed because
-        # some time series data points were logged when the grpc_status
-        # was "UNAVAILABLE" when the client/server were establishing
-        # connections.
-        #
-        # The 'grpc_method' filter condition is needed because the
-        # server metrics are also serving on the Channelz requests.
-        #
-        return (
-            f'metric.type = "{metric_type}" AND '
-            'metric.labels.grpc_status = "OK" AND '
-            f'metric.labels.grpc_method = "{GRPC_METHOD_NAME}"'
-        )
-
-    @classmethod
-    def build_counter_query(cls, metric_type: str) -> str:
-        # For these num rpcs started counter metrics, they do not have the
-        # 'grpc_status' label
-        return (
-            f'metric.type = "{metric_type}" AND '
-            f'metric.labels.grpc_method = "{GRPC_METHOD_NAME}"'
-        )
-
-    def query_metrics(
-        self,
-        metric_names: Iterable[str],
-        build_query_fn: BuildQueryFn,
-        interval: monitoring_v3.TimeInterval,
-    ) -> dict[str, MetricTimeSeries]:
-        """
-        A helper function to make the cloud monitoring API call to query
-        metrics created by this test run.
-        """
-        results = {}
-        for metric in metric_names:
-            logger.info("Requesting list_time_series for metric %s", metric)
-            response = self.metric_client.list_time_series(
-                name=f"projects/{self.project}",
-                filter=build_query_fn(metric),
-                interval=interval,
-                view=monitoring_v3.ListTimeSeriesRequest.TimeSeriesView.FULL,
-            )
-            time_series = list(response)
-
-            self.assertLen(
-                time_series,
-                1,
-                msg=f"Query for {metric} should return exactly 1 time series."
-                f" Found {len(time_series)}.",
-            )
-
-            metric_time_series = MetricTimeSeries.from_response(
-                metric, time_series[0]
-            )
-            logger.info(
-                "Metric %s:\n%s", metric, metric_time_series.pretty_print()
-            )
-            results[metric] = metric_time_series
-        return results
-
     def test_csm_observability(self):
         # TODO(sergiitk): [GAMMA] Consider moving out custom gamma
         #   resource creation out of self.startTestServers()
@@ -446,6 +359,93 @@ def test_csm_observability(self):
                     all_results[metric].points, RESPONSE_PAYLOAD_SIZE
                 )
 
+    @classmethod
+    def build_histogram_query(cls, metric_type: str) -> str:
+        #
+        # The list_time_series API requires us to query one metric
+        # at a time.
+        #
+        # The 'grpc_status = "OK"' filter condition is needed because
+        # some time series data points were logged when the grpc_status
+        # was "UNAVAILABLE" when the client/server were establishing
+        # connections.
+        #
+        # The 'grpc_method' filter condition is needed because the
+        # server metrics are also serving on the Channelz requests.
+        #
+        return (
+            f'metric.type = "{metric_type}" AND '
+            'metric.labels.grpc_status = "OK" AND '
+            f'metric.labels.grpc_method = "{GRPC_METHOD_NAME}"'
+        )
+
+    @classmethod
+    def build_counter_query(cls, metric_type: str) -> str:
+        # For these num rpcs started counter metrics, they do not have the
+        # 'grpc_status' label
+        return (
+            f'metric.type = "{metric_type}" AND '
+            f'metric.labels.grpc_method = "{GRPC_METHOD_NAME}"'
+        )
+
+    def query_metrics(
+        self,
+        metric_names: Iterable[str],
+        build_query_fn: BuildQueryFn,
+        interval: monitoring_v3.TimeInterval,
+    ) -> dict[str, MetricTimeSeries]:
+        """
+        A helper function to make the cloud monitoring API call to query
+        metrics created by this test run.
+        """
+        results = {}
+        for metric in metric_names:
+            logger.info("Requesting list_time_series for metric %s", metric)
+            response = self.metric_client.list_time_series(
+                name=f"projects/{self.project}",
+                filter=build_query_fn(metric),
+                interval=interval,
+                view=monitoring_v3.ListTimeSeriesRequest.TimeSeriesView.FULL,
+            )
+            time_series = list(response)
+
+            self.assertLen(
+                time_series,
+                1,
+                msg=f"Query for {metric} should return exactly 1 time series."
+                f" Found {len(time_series)}.",
+            )
+
+            metric_time_series = MetricTimeSeries.from_response(
+                metric, time_series[0]
+            )
+            logger.info(
+                "Metric %s:\n%s", metric, metric_time_series.pretty_print()
+            )
+            results[metric] = metric_time_series
+        return results
+
+    def assertAtLeastOnePointWithinRange(
+        self,
+        points: list[monitoring_v3.types.Point],
+        ref_bytes: int,
+        tolerance: float = 0.05,
+    ):
+        """
+        A helper function to check whether at least one of the "points" whose
+        mean should be within X% of ref_bytes.
+        """
+        for point in points:
+            if (
+                ref_bytes * (1 - tolerance)
+                < point.value.distribution_value.mean
+                < ref_bytes * (1 + tolerance)
+            ):
+                return
+        self.fail(
+            f"No data point with {ref_bytes}±{tolerance*100}% bytes found"
+        )
+
 
 if __name__ == "__main__":
     absltest.main()