parameters: (self, X, y=None)
return_statement: return logprob
docstring: Compute the log probability under the model.
  
          Parameters
          ----------
          X : array_like, shape (n_samples, n_features)
              List of n_features-dimensional data points. Each row
              corresponds to a single data point.
  
          Returns
          -------
          logprob : array_like, shape (n_samples,)
              Log probabilities of each data point in X
docstring_summary: Compute the log probability under the model.
docstring_tokens: Compute the log probability under the model .
function: def score(self, X, y=None):
          """Compute the log probability under the model.
  
          Parameters
          ----------
          X : array_like, shape (n_samples, n_features)
              List of n_features-dimensional data points. Each row
              corresponds to a single data point.
  
          Returns
          -------
          logprob : array_like, shape (n_samples,)
              Log probabilities of each data point in X
          """
          logprob, _ = self.score_samples(X)
          return logprob
function_tokens: def score ( self , X , y = None ) : logprob , _ = self . score_samples ( X ) return logprob