code: def predict(self, X):
          
          X = _check_2d_inp(X, reshape = True)
          if X.shape[0] == 1:
              return self._predict(X)
          else:
              shape_single = list(X.shape)
              shape_single[0] = 1
              pred = np.empty(X.shape[0], dtype = "int64")
              Parallel(n_jobs=self.njobs, verbose=0, require="sharedmem")(delayed(self._predict)(row, pred, shape_single, X) for row in range(X.shape[0]))
              return pred
docs: Predict the less costly class for a given observation
          
          Note
          ----
          The implementation here happens in a Python loop rather than in some
          NumPy array operations, thus it will be slower than the other algorithms
          here, even though in theory it implies fewer comparisons.
          
          Parameters
          ----------
          X : array (n_samples, n_features)
              Data for which to predict minimum cost label.
          method : str, either 'most-wins' or 'goodness':
              How to decide the best label (see Note)
          
          Returns
          -------
          y_hat : array (n_samples,)
              Label with expected minimum cost for each observation.