code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      # Re-sample to 1hr because default (1min) takes too long to train
      self.csv_name = join(rootdir, "data", "example.csv")
      self.data = csv_to_time_series(
          self.csv_name, timestamp_unit="ms", data_cols=["kpi"]
      )
      logger.info(f"Data looks like:\n{self.data[:5]}")
  
      self.test_len = math.ceil(len(self.data) / 5)
      self.vals_train = self.data[: -self.test_len]
      self.vals_test = self.data[-self.test_len :]
      self.model = SarimaDetector(
          SarimaDetectorConfig(
              order=(2, 0, 2),
              seasonal_order=(2, 0, 0, 24),  # daily seasonality
              transform=TemporalResample("1h"),
              max_forecast_steps=self.test_len,
          )
      )
  
file_path: merlion/tests/anomaly/forecast_based/test_sarima.py
imports: import logging
  import math
  from os.path import abspath, dirname, join
  import sys
  import unittest
  import numpy
  from merlion.models.anomaly.forecast_based.sarima import SarimaDetector, SarimaDetectorConfig
  from merlion.transform.resample import TemporalResample
  from merlion.utils.data_io import csv_to_time_series
  from merlion.utils.time_series import TimeSeries
label_desc: Use image node_id 1 to create a new TestSarima object from inherited base classes: unittest with example: obj = TestSarima()