code: def _generate_sample_from_state(self, state, random_state):
      cur_weights = self.weights_[state]
      i_gauss = random_state.choice(self.n_mix, p=cur_weights)
      if self.covariance_type == "tied":
          # self.covars_.shape == (n_components, n_features, n_features)
          # shouldn't that be (n_mix, ...)?
          covs = self.covars_
      else:
          covs = self.covars_[:, i_gauss]
          covs = fill_covars(
              covs,
              self.covariance_type,
              self.n_components,
              self.n_features,
          )
      return random_state.multivariate_normal(
          self.means_[state, i_gauss], covs[state]
      )
  
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 BaseGMMHMM obj's underlying member method code with example usage: obj._generate_sample_from_state(state, random_state) and returns: random_state