code: def _accumulate_sufficient_statistics(
      self, stats, X, lattice, posteriors, fwdlattice, bwdlattice
  ):
      super()._accumulate_sufficient_statistics(
          stats=stats,
          X=X,
          lattice=lattice,
          posteriors=posteriors,
          fwdlattice=fwdlattice,
          bwdlattice=bwdlattice,
      )
  
      if "e" in self.params:
          if X.shape[1] != 1:
              warnings.warn(
                  "Inputs of shape other than (n_samples, 1) are "
                  "deprecated.",
                  DeprecationWarning,
              )
              X = np.concatenate(X)[:, None]
          np.add.at(stats["obs"].T, X.squeeze(1), posteriors)
  
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 6 for calling the BaseCategoricalHMM obj's underlying member method code with example usage: obj._accumulate_sufficient_statistics(stats, X, lattice, posteriors, fwdlattice, bwdlattice) without return types