parameters: (self, X)
return_statement: return score
docstring: Compute the decision function of ``X``.
  
          Parameters
          ----------
          X : array-like of shape = [n_samples, n_features]
              The input samples.
  
          Returns
          -------
          score : array, shape = [n_samples, n_classes] or [n_samples]
              The decision function of the input samples. The order of the
              classes corresponds to that in the attribute `classes_`.
              Regression and binary classification produce an array of shape
              [n_samples].
docstring_summary: Compute the decision function of ``X``.
docstring_tokens: Compute the decision function of X .
function: def decision_function(self, X):
          """Compute the decision function of ``X``.
  
          Parameters
          ----------
          X : array-like of shape = [n_samples, n_features]
              The input samples.
  
          Returns
          -------
          score : array, shape = [n_samples, n_classes] or [n_samples]
              The decision function of the input samples. The order of the
              classes corresponds to that in the attribute `classes_`.
              Regression and binary classification produce an array of shape
              [n_samples].
          """
          X = check_array(X, dtype=DTYPE, order="C")
          score = self._decision_function(X)
          if score.shape[1] == 1:
              return score.ravel()
          return score
function_tokens: def decision_function ( self , X ) : X = check_array ( X , dtype = DTYPE , order = "C" ) score = self . _decision_function ( X ) if score . shape [ 1 ] == 1 : return score . ravel ( ) return score