class_docstr: Deep autoencoding Gaussian mixture model for anomaly detection (DAGMM).
  DAGMM combines an autoencoder with a Gaussian mixture model to model the distribution
  of the reconstruction errors. DAGMM jointly optimizes the parameters of the deep autoencoder
  and the mixture model simultaneously in an end-to-end fashion.
  
  - paper: `Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho and Haifeng Chen.
    Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. 2018.
    <https://openreview.net/forum?id=BJJLHbb0->`_.
code: def require_even_sampling(self) -> bool:
      return False
  
imports: import copy
  import random
  from typing import List
  import numpy
  import pandas
  from merlion.utils import TimeSeries
  from merlion.models.base import NormalizingConfig
  from merlion.models.anomaly.base import DetectorBase, DetectorConfig, MultipleTimeseriesDetectorMixin
  from merlion.post_process.threshold import AggregateAlarms
  from merlion.utils.misc import ProgressBar, initializer
  from merlion.models.utils.rolling_window_dataset import RollingWindowDataset
label_desc: Use image node_id 2 for calling the DAGMM obj's underlying member method code with example usage: obj.require_even_sampling() and returns: False