docstr: Predict whether a batch of data has drifted from the reference data, given the provided context.
  
  Parameters
  ----------
  x
      Batch of instances.
  c
      Context associated with batch of instances.
  return_p_val
      Whether to return the p-value of the permutation test.
  return_distance
      Whether to return the conditional MMD test statistic between the new batch and reference data.
  return_coupling
      Whether to return the coupling matrices.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the drift prediction and optionally the p-value, threshold, conditional MMD test             statistic and coupling matrices.
docstr_tok: ["Predict","whether","a","batch","of","data","has","drifted","from","the","reference","data",",","given","the","provided","context",".","Parameters","--","--","--","--","--","x","Batch","of","instances",".","c","Context","associated","with","batch","of","instances",".","return_p_val","Whether","to","return","the","p-value","of","the","permutation","test",".","return_distance","Whether","to","return","the","conditional","MMD","test","statistic","between","the","new","batch","and","reference","data",".","return_coupling","Whether","to","return","the","coupling","matrices",".","Returns","--","--","--","-","Dictionary","containing","``","'meta","'","``","and","``","'data","'","``","dictionaries",".","-","``","'meta","'","``","has","the","model","'s","metadata",".","-","``","'data","'","``","contains","the","drift","prediction","and","optionally","the","p-value",",","threshold",",","conditional","MMD","test","statistic","and","coupling","matrices","."]
code: def predict(
      self,  # type: ignore[override]
      x: Union[np.ndarray, list],
      c: np.ndarray,
      return_p_val: bool = True,
      return_distance: bool = True,
      return_coupling: bool = False,
  ) -> Dict[Dict[str, str], Dict[str, Union[int, float]]]:
      """
          Predict whether a batch of data has drifted from the reference data, given the provided context.
  
          Parameters
          ----------
          x
              Batch of instances.
          c
              Context associated with batch of instances.
          return_p_val
              Whether to return the p-value of the permutation test.
          return_distance
              Whether to return the conditional MMD test statistic between the new batch and reference data.
          return_coupling
              Whether to return the coupling matrices.
  
          Returns
          -------
          Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
              - ``'meta'`` has the model's metadata.
              - ``'data'`` contains the drift prediction and optionally the p-value, threshold, conditional MMD test \
              statistic and coupling matrices.
          """
      # compute drift scores
      p_val, dist, distance_threshold, coupling = self.score(x, c)
      drift_pred = int(p_val < self.p_val)
  
      # update reference dataset
      if (
          isinstance(self.update_ref, dict)
          and self.preprocess_fn is not None
          and self.preprocess_at_init
      ):
          x = self.preprocess_fn(x)
      self.x_ref = update_reference(self.x_ref, x, self.n, self.update_ref)  # type: ignore[arg-type]
      self.c_ref = update_reference(
          self.c_ref, c, self.n, self.update_ref
      )
      # used for reservoir sampling
      self.n += len(x)
  
      # populate drift dict
      cd = concept_drift_dict()
      cd["meta"] = self.meta
      cd["data"]["is_drift"] = drift_pred
      if return_p_val:
          cd["data"]["p_val"] = p_val
          cd["data"]["threshold"] = self.p_val
      if return_distance:
          cd["data"]["distance"] = dist
          cd["data"]["distance_threshold"] = distance_threshold
      if return_coupling:
          cd["data"]["coupling_xx"] = coupling[0]
          cd["data"]["coupling_yy"] = coupling[1]
          cd["data"]["coupling_xy"] = coupling[2]
      return cd
  
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 BaseContextMMDDrift obj's underlying member method code with example usage: obj.predict(x, c, return_p_val, return_distance, return_coupling) and returns: cd