class_docstr: Wrapper to load the dataset used for the final round of the IOPs competition
  (http://iops.ai/competition_detail/?competition_id=5).
  
  The dataset contains 29 time series of KPIs gathered from large tech
  companies (Alibaba, Sogou, Tencent, Baidu, and eBay). These time series are
  sampled at either 1min or 5min intervals, and are split into train and test
  sections.
  
  Note that the original competition prohibited algorithms which directly
  hard-coded the KPI ID to set model parameters. So training a new model for
  each time series was against competition rules. They did, however, allow
  algorithms which analyzed each time series (in an automated way), and used
  the results of that automated analysis to perform algorithm/model selection.
docstr: The IOps competition allows anomalies to be detected up to 35min after
  they start. We are currently not using this, but we are leaving the
  override here as a placeholder, if we want to change it later.
code: def max_lag_sec(self):
      """
      The IOps competition allows anomalies to be detected up to 35min after
      they start. We are currently not using this, but we are leaving the
      override here as a placeholder, if we want to change it later.
      """
      return None
  
file_path: merlion/ts_datasets/ts_datasets/anomaly/iops_competition.py
imports: import os
  import zipfile
  import numpy
  import pandas
  from ts_datasets.anomaly.base import TSADBaseDataset
label_desc: Use image node_id 2 for calling the IOpsCompetition obj's underlying member method code with example usage: obj.max_lag_sec() and returns: None