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.
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","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 = False,
  ) -> 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).
          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.
          """
      preds = self._detector.predict(
          x,
          return_p_val,
          return_distance,
          return_probs,
          return_model=True,
      )
      preds["data"]["diffs"] = (
          preds["data"]["model"].diffs.detach().cpu().numpy()
      )
      preds["data"]["diff_coeffs"] = (
          preds["data"]["model"].coeffs.detach().cpu().numpy()
      )
      if not return_model:
          del preds["data"]["model"]
      return preds
  
file_path: alibi-detect/alibi_detect/cd/pytorch/spot_the_diff.py
imports: import logging
  import numpy
  import torch
  import torch.nn
  from torch.utils.data import DataLoader
  from typing import Callable, Dict, Optional, Union
  from alibi_detect.cd.pytorch.classifier import ClassifierDriftTorch
  from alibi_detect.utils.pytorch.data import TorchDataset
  from alibi_detect.utils.pytorch import GaussianRBF
  from alibi_detect.utils.pytorch.prediction import predict_batch
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 2 for calling the SpotTheDiffDriftTorch obj's underlying member method code with example usage: obj.predict(x, return_p_val, return_distance, return_probs, return_model) and returns: preds