docstr: Predict whether a batch of data has drifted from the reference data.
  
  Parameters
  ----------
  x
      Batch of instances.
  drift_type
      Predict drift at the 'feature' or 'batch' level. For 'batch', the test statistics for
      each feature are aggregated using the Bonferroni or False Discovery Rate correction (if n_features>1).
  return_p_val
      Whether to return feature level p-values.
  return_distance
      Whether to return the test statistic between the features of the new batch and reference data.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the drift prediction and optionally the feature level p-values, threshold after             multivariate correction if needed and test statistics.
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code: def predict(
      self,
      x: Union[np.ndarray, list],
      drift_type: str = "batch",
      return_p_val: bool = True,
      return_distance: bool = True,
  ) -> Dict[Dict[str, str], Dict[str, Union[np.ndarray, int, float]]]:
      """
          Predict whether a batch of data has drifted from the reference data.
  
          Parameters
          ----------
          x
              Batch of instances.
          drift_type
              Predict drift at the 'feature' or 'batch' level. For 'batch', the test statistics for
              each feature are aggregated using the Bonferroni or False Discovery Rate correction (if n_features>1).
          return_p_val
              Whether to return feature level p-values.
          return_distance
              Whether to return the test statistic between the features of the new batch and reference data.
  
          Returns
          -------
          Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
              - ``'meta'`` has the model's metadata.
              - ``'data'`` contains the drift prediction and optionally the feature level p-values, threshold after \
              multivariate correction if needed and test statistics.
          """
      # compute drift scores
      p_vals, dist = self.score(x)
  
      # TODO: return both feature-level and batch-level drift predictions by default
      # values below p-value threshold are drift
      if drift_type == "feature":
          drift_pred = (p_vals < self.p_val).astype(int)
      elif drift_type == "batch" and self.correction == "bonferroni":
          threshold = self.p_val / self.n_features
          drift_pred = int((p_vals < threshold).any())  # type: ignore[assignment]
      elif drift_type == "batch" and self.correction == "fdr":
          drift_pred, threshold = fdr(p_vals, q_val=self.p_val)  # type: ignore[assignment]
      else:
          raise ValueError(
              "`drift_type` needs to be either `feature` or `batch`."
          )
  
      # 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_vals
          cd["data"]["threshold"] = (
              self.p_val if drift_type == "feature" else threshold
          )
      if return_distance:
          cd["data"]["distance"] = dist
      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 BaseUnivariateDrift obj's underlying member method code with example usage: obj.predict(x, drift_type, return_p_val, return_distance) and returns: cd