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).
  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 detector's metadata.
      - ``'data'`` contains the drift prediction, the diffs used to distinguish reference from test instances,             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 well as the associated reference and test instances of the out-of-fold predictions,             and the trained model.
code: def predict(
      self,
      x: np.ndarray,
      return_p_val: bool = True,
      return_distance: bool = True,
      return_probs: bool = True,
      return_model: bool = True,
  ) -> Dict[str, Dict[str, Union[int, str, 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).
          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 detector's metadata.
              - ``'data'`` contains the drift prediction, the diffs used to distinguish reference from test instances, \
              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 well as the associated reference and test instances of the out-of-fold predictions, \
              and the trained model.
          """
      return self._detector.predict(
          x, return_p_val, return_distance, return_probs, return_model
      )
  
file_path: alibi-detect/alibi_detect/cd/spot_the_diff.py
imports: import numpy
  from typing import Callable, Dict, Optional, Union
  from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, 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 SpotTheDiffDrift obj's underlying member method code with example usage: obj.predict(x, return_p_val, return_distance, return_probs, return_model) and returns: self