class_docstr: Accumulator which maintains summary statistics describing an anomaly
  detection algorithm's performance. Can be used to compute many different
  time series anomaly detection metrics.
code: def __init__(
      self,
      num_tp_anom=0,
      num_tp_pointwise=0,
      num_tp_point_adj=0,
      num_fn_anom=0,
      num_fn_pointwise=0,
      num_fn_point_adj=0,
      num_fp=0,
      num_tn=0,
      tp_score=0.0,
      fp_score=0.0,
      tp_detection_delays=None,
      tp_anom_durations=None,
      anom_durations=None,
  ):
      self.num_tp_anom = num_tp_anom
      self.num_tp_pointwise = num_tp_pointwise
      self.num_tp_point_adj = num_tp_point_adj
      self.num_fn_anom = num_fn_anom
      self.num_fn_pointwise = num_fn_pointwise
      self.num_fn_point_adj = num_fn_point_adj
      self.num_fp = num_fp
      self.num_tn = num_tn
      self.tp_score = tp_score
      self.fp_score = fp_score
      self.tp_detection_delays = tp_detection_delays or []
      self.tp_anom_durations = tp_anom_durations or []
      self.anom_durations = anom_durations or []
  
imports: from bisect import bisect_left
  from enum import Enum
  from functools import partial
  from typing import Tuple, Union
  import numpy
  import pandas
  from merlion.evaluate.base import EvaluatorBase, EvaluatorConfig
  from merlion.utils import TimeSeries, UnivariateTimeSeries
  from merlion.utils.misc import call_with_accepted_kwargs
label_desc: Use image node_id 1 to create a new TSADScoreAccumulator object  with example: obj = TSADScoreAccumulator(num_tp_anom, num_tp_pointwise, num_tp_point_adj, num_fn_anom, num_fn_pointwise, num_fn_point_adj, num_fp, num_tn, tp_score, fp_score, tp_detection_delays, tp_anom_durations, anom_durations)