docstr: Predict whether a batch of data has drifted from the reference data.
  
  Parameters
  ----------
  x
      Batch of instances.
  return_p_val
      Whether to return the p-value of the permutation test.
  return_distance
      Whether to return the MMD metric between the new batch and reference data.
  return_kernel
      Whether to return the updated kernel trained to discriminate reference and test instances.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the detector's metadata.
      - ``'data'`` contains the drift prediction and optionally the p-value, threshold, MMD metric and             trained kernel.
docstr_tok: ["Predict","whether","a","batch","of","data","has","drifted","from","the","reference","data",".","Parameters","--","--","--","--","--","x","Batch","of","instances",".","return_p_val","Whether","to","return","the","p-value","of","the","permutation","test",".","return_distance","Whether","to","return","the","MMD","metric","between","the","new","batch","and","reference","data",".","return_kernel","Whether","to","return","the","updated","kernel","trained","to","discriminate","reference","and","test","instances",".","Returns","--","--","--","-","Dictionary","containing","``","'meta","'","``","and","``","'data","'","``","dictionaries",".","-","``","'meta","'","``","has","the","detector","'s","metadata",".","-","``","'data","'","``","contains","the","drift","prediction","and","optionally","the","p-value",",","threshold",",","MMD","metric","and","trained","kernel","."]
code: def predict(
      self,
      x: Union[np.ndarray, list],
      return_p_val: bool = True,
      return_distance: bool = True,
      return_kernel: bool = True,
  ) -> Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]]:
      """
          Predict whether a batch of data has drifted from the reference data.
  
          Parameters
          ----------
          x
              Batch of instances.
          return_p_val
              Whether to return the p-value of the permutation test.
          return_distance
              Whether to return the MMD metric between the new batch and reference data.
          return_kernel
              Whether to return the updated kernel trained to discriminate reference and test instances.
  
          Returns
          -------
          Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
              - ``'meta'`` has the detector's metadata.
              - ``'data'`` contains the drift prediction and optionally the p-value, threshold, MMD metric and \
              trained kernel.
          """
      # compute drift scores
      p_val, dist, distance_threshold = self.score(x)
      drift_pred = int(p_val < self.p_val)
  
      # update reference dataset
      if (
          isinstance(self.update_x_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_x_ref)  # type: ignore[arg-type]
      # 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_kernel:
          cd["data"]["kernel"] = self.kernel
      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 5 for calling the BaseLearnedKernelDrift obj's underlying member method code with example usage: obj.predict(x, return_p_val, return_distance, return_kernel) and returns: cd