class_docstr: Configuration class for DAGMM. The normalization is inherited from `NormalizingConfig`.
  The input data will be standardized automatically.
docstr: :param gmm_k: The number of Gaussian distributions
  :param hidden_size: The hidden size of the autoencoder module in DAGMM
  :param sequence_len: The input series length, e.g., input = [x(t-sequence_len+1)...,x(t-1),x(t)]
  :param lambda_energy: The regularization weight for the energy term
  :param lambda_cov_diag: The regularization weight for the covariance diagonal entries
  :param lr: The learning rate during training
  :param batch_size: The batch size during training
  :param num_epochs: The number of training epochs
code: def __init__(
      self,
      gmm_k: int = 3,
      hidden_size: int = 5,
      sequence_len: int = 1,
      lambda_energy: float = 0.1,
      lambda_cov_diag: float = 0.005,
      lr: float = 1e-3,
      batch_size: int = 256,
      num_epochs: int = 10,
      **kwargs,
  ):
      """
      :param gmm_k: The number of Gaussian distributions
      :param hidden_size: The hidden size of the autoencoder module in DAGMM
      :param sequence_len: The input series length, e.g., input = [x(t-sequence_len+1)...,x(t-1),x(t)]
      :param lambda_energy: The regularization weight for the energy term
      :param lambda_cov_diag: The regularization weight for the covariance diagonal entries
      :param lr: The learning rate during training
      :param batch_size: The batch size during training
      :param num_epochs: The number of training epochs
      """
      super().__init__(**kwargs)
  
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 1 to create a new DAGMMConfig object from inherited base classes: DetectorConfig, NormalizingConfig with example: obj = DAGMMConfig(gmm_k, hidden_size, sequence_len, lambda_energy, lambda_cov_diag, lr, batch_size, num_epochs)