code: def score(self, X, y):
      y_pred = self.predict(X)
      return np.mean(np.abs(y - y_pred))
  
imports: import numpy
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.metrics.pairwise import pairwise_kernels
  from .base import BaseEstimator
  from .dataset_fast import get_dataset
  from .prank_fast import _prank_fit
  from .prank_fast import _prank_fit_kernel
  from .prank_fast import _prank_predict
label_desc: Use image node_id 1 for calling the _BasePRank obj's underlying member method code with example usage: obj.score(X, y) and returns: np