docstr: Compute the feature-wise drift score which is the p-value of the
  statistical test and the test statistic.
  
  Parameters
  ----------
  x
      Batch of instances.
  
  Returns
  -------
  Feature level p-values and test statistics.
docstr_tok: ["Compute","the","feature-wise","drift","score","which","is","the","p-value","of","the","statistical","test","and","the","test","statistic",".","Parameters","--","--","--","--","--","x","Batch","of","instances",".","Returns","--","--","--","-","Feature","level","p-values","and","test","statistics","."]
code: def score(
      self, x: Union[np.ndarray, list]
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Compute the feature-wise drift score which is the p-value of the
      statistical test and the test statistic.
  
      Parameters
      ----------
      x
          Batch of instances.
  
      Returns
      -------
      Feature level p-values and test statistics.
      """
      x_ref, x = self.preprocess(x)
      score, dist = self.feature_score(
          x_ref, x
      )  # feature-wise univariate test
      return score, dist
  
imports: import logging
  from abc import abstractmethod
  from typing import Callable, Dict, List, Optional, Tuple, Union, Any
  import numpy
  from alibi_detect.base import BaseDetector, concept_drift_dict, DriftConfigMixin
  from alibi_detect.cd.utils import get_input_shape, update_reference
  from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow
  from alibi_detect.utils.statstest import fdr
  from scipy.stats import binom_test, ks_2samp
  from sklearn.model_selection import StratifiedKFold
label_desc: Use image node_id 4 for calling the BaseUnivariateDrift obj's underlying member method code with example usage: obj.score(x) and returns: score,  dist