docstr: Predict whether the most recent window(s) of data have drifted from the reference data.
  
  Parameters
  ----------
  x_t
      A single instance to be added to the test-window(s).
  return_test_stat
      Whether to return the test statistic and threshold.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the drift prediction and optionally the test-statistic and threshold.
docstr_tok: ["Predict","whether","the","most","recent","window","(","s",")","of","data","have","drifted","from","the","reference","data",".","Parameters","--","--","--","--","--","x_t","A","single","instance","to","be","added","to","the","test-window","(","s",")",".","return_test_stat","Whether","to","return","the","test","statistic","and","threshold",".","Returns","--","--","--","-","Dictionary","containing","``","'meta","'","``","and","``","'data","'","``","dictionaries",".","-","``","'meta","'","``","has","the","model","'s","metadata",".","-","``","'data","'","``","contains","the","drift","prediction","and","optionally","the","test-statistic","and","threshold","."]
code: def predict(
      self,
      x_t: Union[np.ndarray, Any],
      return_test_stat: bool = True,
  ) -> Dict[Dict[str, str], Dict[str, Union[int, float]]]:
      """
      Predict whether the most recent window(s) of data have drifted from the reference data.
  
      Parameters
      ----------
      x_t
          A single instance to be added to the test-window(s).
      return_test_stat
          Whether to return the test statistic and threshold.
  
      Returns
      -------
      Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
          - ``'meta'`` has the model's metadata.
          - ``'data'`` contains the drift prediction and optionally the test-statistic and threshold.
      """
      # Compute test stat and check for drift
      test_stats = self.score(x_t)
      thresholds = self.get_threshold(
          self.t - 1
      )  # Note t-1 here, has we wish to use the unconditional thresholds
      drift_pred = self._check_drift(test_stats, thresholds)
  
      # Update results attributes
      self.test_stats = np.concatenate(
          [self.test_stats, test_stats[None, :, :]]
      )
      self.drift_preds = np.concatenate(
          [self.drift_preds, np.array([drift_pred])]
      )
  
      # populate drift dict
      cd = concept_drift_dict()
      cd["meta"] = self.meta
      cd["data"]["is_drift"] = drift_pred
      cd["data"]["time"] = self.t
      cd["data"]["ert"] = self.ert
      if return_test_stat:
          cd["data"]["test_stat"] = test_stats
          cd["data"]["threshold"] = thresholds
  
      return cd
  
imports: import logging
  import warnings
  from abc import abstractmethod
  from typing import Any, Callable, Dict, List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from alibi_detect.base import BaseDetector, concept_drift_dict
  from alibi_detect.cd.utils import get_input_shape
  from alibi_detect.utils.state import StateMixin
  from alibi_detect.utils._types import Literal
label_desc: Use image node_id 12 for calling the BaseUniDriftOnline obj's underlying member method code with example usage: obj.predict(x_t, return_test_stat) and returns: cd