code: def predict(self, X):  # pylint: disable=no-self-use
      # convert predictions of individual models into ranks
      ranks = np.apply_along_axis(rankdata, 0, X)
      # average predicted ranks
      return ranks.mean(axis=X.ndim - 1)
  
imports: import numbers
  from joblib import Parallel, delayed
  import numpy
  from scipy.stats import kendalltau, rankdata, spearmanr
  from sklearn.base import BaseEstimator, clone
  from sklearn.model_selection import check_cv
  from sklearn.utils._param_validation import Interval, StrOptions
  from .base import _fit_and_score
  from .stacking import Stacking
label_desc: Use image node_id 2 for calling the MeanRankEstimator obj's underlying member method code with example usage: obj.predict(X) and returns: ranks