code: def _initialize_sufficient_statistics(self):
      stats = super()._initialize_sufficient_statistics()
      stats["post_mix_sum"] = np.zeros((self.n_components, self.n_mix))
      stats["post_sum"] = np.zeros(self.n_components)
  
      if "m" in self.params:
          lambdas, mus = self.means_weight, self.means_prior
          stats["m_n"] = lambdas[:, :, None] * mus
      if "c" in self.params:
          stats["c_n"] = np.zeros_like(self.covars_)
  
      # These statistics are stored in arrays and updated in-place.
      # We accumulate chunks of data for multiple sequences (aka
      # multiple frames) during fitting. The fit(X, lengths) method
      # in the BaseHMM class will call
      # _accumulate_sufficient_statistics once per sequence in the
      # training samples. Data from all sequences needs to be
      # accumulated and fed into _do_mstep.
      return stats
  
imports: import functools
  import inspect
  import warnings
  import numpy
  from scipy import special
  from scipy.stats import multinomial, poisson
  from sklearn.utils import check_random_state
  from .base import BaseHMM, _AbstractHMM
  from .stats import log_multivariate_normal_density
  from .utils import fill_covars, log_normalize
label_desc: Use image node_id 4 for calling the BaseGMMHMM obj's underlying member method code with example usage: obj._initialize_sufficient_statistics() and returns: stats