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 test.
  return_distance
      Whether to return a notion of strength of the drift.
      K-S test stat if binarize_preds=False, otherwise relative error reduction.
  return_probs
      Whether to return the instance level classifier probabilities for the reference and test data
      (0=reference data, 1=test data). The reference and test instances of the associated
      probabilities are also returned.
  return_model
      Whether to return the updated model trained to discriminate reference and test instances.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the drift prediction and optionally the p-value, performance of the classifier             relative to its expectation under the no-change null, the out-of-fold classifier model             prediction probabilities on the reference and test data as well as the associated reference             and test instances of the out-of-fold predictions, and the trained model.
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code: def predict(
      self,
      x: Union[np.ndarray, list],
      return_p_val: bool = True,
      return_distance: bool = True,
      return_probs: bool = True,
      return_model: bool = True,
  ) -> Dict[str, Dict[str, Union[str, 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 test.
          return_distance
              Whether to return a notion of strength of the drift.
              K-S test stat if binarize_preds=False, otherwise relative error reduction.
          return_probs
              Whether to return the instance level classifier probabilities for the reference and test data
              (0=reference data, 1=test data). The reference and test instances of the associated
              probabilities are also returned.
          return_model
              Whether to return the updated model trained to discriminate reference and test instances.
  
          Returns
          -------
          Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
              - ``'meta'`` has the model's metadata.
              - ``'data'`` contains the drift prediction and optionally the p-value, performance of the classifier \
              relative to its expectation under the no-change null, the out-of-fold classifier model \
              prediction probabilities on the reference and test data as well as the associated reference \
              and test instances of the out-of-fold predictions, and the trained model.
          """
      # compute drift scores
      (
          p_val,
          dist,
          probs_ref,
          probs_test,
          x_ref_oof,
          x_test_oof,
      ) = 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)
      # TODO: TBD: can `x` ever be a `list` after pre-processing? update_references and downstream functions
      # don't support list inputs and without the type: ignore[arg-type] mypy complains
      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
      if return_probs:
          cd["data"]["probs_ref"] = probs_ref
          cd["data"]["probs_test"] = probs_test
          cd["data"]["x_ref_oof"] = x_ref_oof
          cd["data"]["x_test_oof"] = x_test_oof
      if return_model:
          cd["data"]["model"] = self.model
      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 6 for calling the BaseClassifierDrift obj's underlying member method code with example usage: obj.predict(x, return_p_val, return_distance, return_probs, return_model) and returns: cd