class_docstr: The VAE-based multivariate time series anomaly detector.
  This detector utilizes a variational autoencoder to infer the correlations between
  different time series and estimate the distribution of the reconstruction errors
  for anomaly detection.
  
  - paper: `Diederik P Kingma and Max Welling. Auto-Encoding Variational Bayes. 2013.
    <https://arxiv.org/abs/1312.6114>`_
class_docstr_tok: ["The","VAE-based","multivariate","time","series","anomaly","detector",".","This","detector","utilizes","a","variational","autoencoder","to","infer","the","correlations","between","different","time","series","and","estimate","the","distribution","of","the","reconstruction","errors","for","anomaly","detection",".","-","paper",":","`","Diederik","P","Kingma","and","Max","Welling",".","Auto-Encoding","Variational","Bayes",".","2013",".","<","https",":","\/\/arxiv.org\/abs\/1312.6114",">","`","_"]
code: def require_even_sampling(self) -> bool:
      return False
  
imports: from typing import Sequence
  import numpy
  import pandas
  from merlion.models.base import NormalizingConfig
  from merlion.models.anomaly.base import DetectorBase, DetectorConfig
  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 VAE obj's underlying member method code with example usage: obj.require_even_sampling() and returns: False