class_docstr: Hidden Markov Model with Multivariate Gaussian Emissions trained
  using Variational Inference.
  
  References:
      * https://arxiv.org/abs/1605.08618
      * https://core.ac.uk/reader/10883750
      * https://theses.gla.ac.uk/6941/7/2005McGroryPhD.pdf
  
  Attributes
  ----------
  n_features : int
      Dimensionality of the Gaussian emissions.
  
  monitor_ : ConvergenceMonitor
      Monitor object used to check the convergence of EM.
  
  startprob_prior_ : array, shape (n_components, )
      Prior for the initial state occupation distribution.
  
  startprob_posterior_ : array, shape (n_components, )
      Posterior estimate of the state occupation distribution.
  
  transmat_prior_ : array, shape (n_components, n_components)
      Prior for the matrix of transition probabilities between states.
  
  transmat_posterior_ : array, shape (n_components, n_components)
      Posterior estimate of the transition probabilities between states.
  
  means_prior_: array, shape (n_components, n_features)
      Prior estimates for the mean of each state.
  
  means_posterior_: array, shape (n_components, n_features)
      Posterior estimates for the mean of each state.
  
  beta_prior_: array, shape (n_components, )
      Prior estimate on the scale of the variance over the means.
  
  beta_posterior_: array, shape (n_components, )
      Posterior estimate of the scale of the variance over the means.
  
  covars_ : array
      Covariance parameters for each state.
  
      The shape depends on :attr:`covariance_type`:
  
      * (n_components, )                        if "spherical",
      * (n_components, n_features)              if "diag",
      * (n_components, n_features, n_features)  if "full",
      * (n_features, n_features)                if "tied".
  
  dof_prior_: int / array
      The Degrees Of Freedom prior for each state's Wishart distribution.
      The type depends on :attr:`covariance_type`:
  
      * array, shape (n_components, )  if "full",
      * int                            if "tied".
  
  dof_prior_: int / array
      The Prior on the Degrees Of Freedom
      for each state's Wishart distribution.
      The type depends on :attr:`covariance_type`:
  
      * array, shape (n_components, )  if "full",
      * int                            if "tied".
  
  dof_posterior_: int / array
      The Degrees Of Freedom for each state's Wishart distribution.
      The type depends on :attr:`covariance_type`:
  
      * array, shape (n_components, )  if "full",
      * int                            if "tied".
  
  scale_prior_ : array
      Prior for the Inverse scale parameter for each state's
      Wishart distribution. The wishart distribution is
      the conjugate prior for the covariance.
  
      The shape depends on :attr:`covariance_type`:
  
      * (n_components, )                        if "spherical",
      * (n_components, n_features)              if "diag",
      * (n_components, n_features, n_features)  if "full",
      * (n_features, n_features)                if "tied".
  
  scale_posterior_ : array
      Inverse scale parameter for each state's wishart distribution.
      The wishart distribution is the conjugate prior for the covariance.
  
      The shape depends on :attr:`covariance_type`:
  
      * (n_components, )                        if "spherical",
      * (n_components, n_features)              if "diag",
      * (n_components, n_features, n_features)  if "full",
      * (n_features, n_features)                if "tied".
  
  Examples
  --------
  >>> from hmmlearn.hmm import VariationalGaussianHMM
  >>> VariationalGaussianHMM(n_components=2)  #doctest: +ELLIPSIS
  VariationalGaussianHMM(algorithm='viterbi',...
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code: def covars_(self, covars):
      covars = np.array(covars, copy=True)
      _utils._validate_covars(
          covars, self.covariance_type, self.n_components
      )
      self._covars_ = covars
  
imports: import logging
  import numbers
  import numpy
  from scipy import special
  from sklearn import cluster
  from sklearn.utils import check_random_state
  from .None import _kl_divergence, _utils
  from ._emissions import BaseCategoricalHMM, BaseGaussianHMM
  from .base import VariationalBaseHMM
  from .hmm import COVARIANCE_TYPES
  from .utils import fill_covars
label_desc: Use image node_id 3 for calling the VariationalGaussianHMM obj's underlying member method code with example usage: obj.covars_(covars) without return types