class_docstr: Base class for mixture models.
  
  This abstract class specifies an interface for all mixture classes and
  provides basic common methods for mixture models.
code: def __init__(
      self,
      n_components,
      tol,
      reg_covar,
      max_iter,
      n_init,
      init_params,
      random_state,
      warm_start,
      verbose,
      verbose_interval,
  ):
      self.n_components = n_components
      self.tol = tol
      self.reg_covar = reg_covar
      self.max_iter = max_iter
      self.n_init = n_init
      self.init_params = init_params
      self.random_state = random_state
      self.warm_start = warm_start
      self.verbose = verbose
      self.verbose_interval = verbose_interval
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  from time import time
  import numpy
  from scipy.special import logsumexp
  from ..None import cluster
  from ..base import BaseEstimator, DensityMixin, _fit_context
  from ..cluster import kmeans_plusplus
  from ..exceptions import ConvergenceWarning
  from ..utils import check_random_state
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 1 to create a new BaseMixture object from inherited base classes: DensityMixin, BaseEstimator with example: obj = BaseMixture(n_components, tol, reg_covar, max_iter, n_init, init_params, random_state, warm_start, verbose, verbose_interval)