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 permutation test.
  return_distance
      Whether to return the LSDD metric between 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 p-value, threshold and LSDD metric.
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","permutation","test",".","return_distance","Whether","to","return","the","LSDD","metric","between","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","p-value",",","threshold","and","LSDD","metric","."]
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[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 permutation test.
      return_distance
          Whether to return the LSDD metric between 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 p-value, threshold and LSDD metric.
      """
      return self._detector.predict(x, return_p_val, return_distance)
  
file_path: alibi-detect/alibi_detect/cd/lsdd.py
imports: import numpy
  from typing import Callable, Dict, Optional, Union, Tuple
  from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
  from alibi_detect.utils.warnings import deprecated_alias
  from alibi_detect.base import DriftConfigMixin
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 2 for calling the LSDDDrift obj's underlying member method code with example usage: obj.predict(x, return_p_val, return_distance) and returns: self