code: def _compute_log_likelihood(self, X):
      logprobs = np.empty((len(X), self.n_components))
      for c in range(self.n_components):
          logprobs[:, c] = poisson.logpmf(X, self.lambdas_[c]).sum(
              axis=1
          )
      return logprobs
  
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 BasePoissonHMM obj's underlying member method code with example usage: obj._compute_log_likelihood(X) and returns: logprobs