docstr: Compute the MMD based conditional test statistic, and perform a conditional permutation test to obtain a
  p-value representing the test statistic's extremity under the null hypothesis.
  
  Parameters
  ----------
  x
      Batch of instances.
  c
      Context associated with batch of instances.
  
  Returns
  -------
  p-value obtained from the conditional permutation test, the conditional MMD test statistic, the test         statistic threshold above which drift is flagged, and a tuple containing the coupling matrices         :math:`(W_{ref,ref}, W_{test,test}, W_{ref,test})`.
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code: def score(
      self, x: Union[np.ndarray, list], c: np.ndarray
  ) -> Tuple[float, float, float, Tuple]:
      """
          Compute the MMD based conditional test statistic, and perform a conditional permutation test to obtain a
          p-value representing the test statistic's extremity under the null hypothesis.
  
          Parameters
          ----------
          x
              Batch of instances.
          c
              Context associated with batch of instances.
  
          Returns
          -------
          p-value obtained from the conditional permutation test, the conditional MMD test statistic, the test \
          statistic threshold above which drift is flagged, and a tuple containing the coupling matrices \
          :math:`(W_{ref,ref}, W_{test,test}, W_{ref,test})`.
          """
      return self._detector.score(x, c)
  
file_path: alibi-detect/alibi_detect/cd/context_aware.py
imports: import logging
  import numpy
  from typing import Callable, Dict, Optional, Union, Tuple
  from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
  from alibi_detect.utils.warnings import deprecated_alias
  from alibi_detect.base import DriftConfigMixin
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 3 for calling the ContextMMDDrift obj's underlying member method code with example usage: obj.score(x, c) and returns: self