code: def _compute_log_weighted_gaussian_densities(self, X, i_comp):
      cur_means = self.means_[i_comp]
      cur_covs = self.covars_[i_comp]
      if self.covariance_type == "spherical":
          cur_covs = cur_covs[:, None]
      log_cur_weights = np.log(self.weights_[i_comp])
  
      return (
          log_multivariate_normal_density(
              X, cur_means, cur_covs, self.covariance_type
          )
          + log_cur_weights
      )
  
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 2 for calling the BaseGMMHMM obj's underlying member method code with example usage: obj._compute_log_weighted_gaussian_densities(X, i_comp) and returns: unknown