code: def _compute_likelihood(self, X):
      probs = np.empty((len(X), self.n_components))
      n_trials = X.sum(axis=1)
      for c in range(self.n_components):
          probs[:, c] = multinomial.pmf(
              X, n=n_trials, p=self.emissionprob_[c, :]
          )
      return probs
  
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 3 for calling the BaseMultinomialHMM obj's underlying member method code with example usage: obj._compute_likelihood(X) and returns: probs