parameters: (self, X, y=None)
return_statement: return np.sum(self.score_samples(X))
docstring: Compute the total log probability density 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.
          y : None
              Ignored. This parameter exists only for compatibility with
              :class:`sklearn.pipeline.Pipeline`.
  
          Returns
          -------
          logprob : float
              Total log-likelihood of the data in X. This is normalized to be a
              probability density, so the value will be low for high-dimensional
              data.
docstring_summary: Compute the total log probability density under the model.
docstring_tokens: Compute the total log probability density under the model .
function: def score(self, X, y=None):
          """Compute the total log probability density 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.
          y : None
              Ignored. This parameter exists only for compatibility with
              :class:`sklearn.pipeline.Pipeline`.
  
          Returns
          -------
          logprob : float
              Total log-likelihood of the data in X. This is normalized to be a
              probability density, so the value will be low for high-dimensional
              data.
          """
          return np.sum(self.score_samples(X))
function_tokens: def score ( self , X , y = None ) : return np . sum ( self . score_samples ( X ) )