class_docstr: Dynamic baseline-based anomaly detector.
  
  Detects anomalies by comparing data to historical data that has occurred in
  the same window of time, as defined by any combination of time of day,
  day of week, or day of month.
  
  A DBL model can have a fixed period or a dynamic rolling period. A fixed
  period model trains its baselines exclusively on datapoints occurring in the
  fixed period, while a rolling period model trains continually on the most
  recent datapoints within its ``train-window``.
class_docstr_tok: ["Dynamic","baseline-based","anomaly","detector",".","Detects","anomalies","by","comparing","data","to","historical","data","that","has","occurred","in","the","same","window","of","time",",","as","defined","by","any","combination","of","time","of","day",",","day","of","week",",","or","day","of","month",".","A","DBL","model","can","have","a","fixed","period","or","a","dynamic","rolling","period",".","A","fixed","period","model","trains","its","baselines","exclusively","on","datapoints","occurring","in","the","fixed","period",",","while","a","rolling","period","model","trains","continually","on","the","most","recent","datapoints","within","its","``","train-window","``","."]
code: def require_even_sampling(self) -> bool:
      return False
  
imports: import logging
  from itertools import chain, product
  from merlion.plot import Figure
  from typing import Any, Dict, List, Tuple
  from enum import Enum, auto
  from operator import xor
  from math import ceil
  import numpy
  import pandas
  from merlion.evaluate.anomaly import TSADMetric
  from merlion.models.anomaly.base import DetectorConfig, DetectorBase
  from merlion.utils import UnivariateTimeSeries, TimeSeries
  from merlion.utils.istat import Mean, Variance
  from merlion.utils.resample import to_pd_datetime, to_timestamp
label_desc: Use image node_id 2 for calling the DynamicBaseline obj's underlying member method code with example usage: obj.require_even_sampling() and returns: False