code: def test_alarm(self):
      # alarm function returns the post-rule processed anomaly scores
      print("-" * 80)
      logger.info("test_alarm\n" + "-" * 80 + "\n")
      logger.info("Training model...")
      self.model.train(self.vals_train)
      alarms = self.model.get_anomaly_label(self.vals_test)
      n_alarms = np.sum(alarms.to_pd().values != 0)
      logger.info(f"Alarms look like:\n{alarms[:5]}")
      logger.info(f"Number of alarms: {n_alarms}\n")
      self.assertLessEqual(n_alarms, 2)
  
file_path: merlion/tests/anomaly/forecast_based/test_arima.py
imports: import logging
  import math
  from os.path import abspath, dirname, join
  import sys
  import unittest
  import numpy
  from merlion.transform.resample import TemporalResample
  from merlion.models.anomaly.forecast_based.arima import ArimaDetector, ArimaDetectorConfig
  from merlion.utils.time_series import TimeSeries
  from merlion.utils.data_io import csv_to_time_series
label_desc: Use image node_id 3 for calling the TestArima obj's underlying member method code with example usage: obj.test_alarm() without return types