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 the K-S test statistic
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the drift prediction and optionally the p-value, threshold and test statistic.
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","test",".","return_distance","Whether","to","return","the","K-S","test","statistic","Returns","--","--","--","-","Dictionary","containing","``","'meta","'","``","and","``","'data","'","``","dictionaries",".","-","``","'meta","'","``","has","the","model","'s","metadata",".","-","``","'data","'","``","contains","the","drift","prediction","and","optionally","the","p-value",",","threshold","and","test","statistic","."]
code: def predict(
      self,
      x: Union[np.ndarray, list],
      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.
      return_p_val
          Whether to return the p-value of the test.
      return_distance
          Whether to return the K-S test statistic
  
      Returns
      -------
      Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
          - ``'meta'`` has the model's metadata.
          - ``'data'`` contains the drift prediction and optionally the p-value, threshold and test statistic.
      """
      return self._detector.predict(
          x, return_p_val=return_p_val, return_distance=return_distance
      )
  
file_path: alibi-detect/alibi_detect/cd/model_uncertainty.py
imports: import logging
  import numpy
  from typing import Callable, Dict, Optional, Union
  from functools import partial
  from alibi_detect.cd.ks import KSDrift
  from alibi_detect.cd.chisquare import ChiSquareDrift
  from alibi_detect.cd.preprocess import classifier_uncertainty, regressor_uncertainty
  from alibi_detect.cd.utils import encompass_batching, encompass_shuffling_and_batch_filling
  from alibi_detect.utils.frameworks import BackendValidator, Framework
  from alibi_detect.base import DriftConfigMixin
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 2 for calling the RegressorUncertaintyDrift obj's underlying member method code with example usage: obj.predict(x, return_p_val, return_distance) and returns: self