class_docstr: Base class for sklearn backend outlier detection algorithms.
class_docstr_tok: ["Base","class","for","sklearn","backend","outlier","detection","algorithms","."]
docstr: Converts data from the frontend to the backend format.
  
  This is an interface method that ensures that the input of the chosen outlier detector backend is in the correct
  format. In the case of the Sklearn backend, we ensure the data is a numpy array.
  
  Parameters
  ----------
  x
      Data to convert.
docstr_tok: ["Converts","data","from","the","frontend","to","the","backend","format",".","This","is","an","interface","method","that","ensures","that","the","input","of","the","chosen","outlier","detector","backend","is","in","the","correct","format",".","In","the","case","of","the","Sklearn","backend",",","we","ensure","the","data","is","a","numpy","array",".","Parameters","--","--","--","--","--","x","Data","to","convert","."]
code: def _to_backend_dtype(x: Union[List, np.ndarray]) -> np.ndarray:
      """Converts data from the frontend to the backend format.
  
      This is an interface method that ensures that the input of the chosen outlier detector backend is in the correct
      format. In the case of the Sklearn backend, we ensure the data is a numpy array.
  
      Parameters
      ----------
      x
          Data to convert.
      """
      return np.asarray(x)
  
imports: from typing import List, Union, Optional, Dict
  from dataclasses import dataclass, asdict
  from abc import ABC, abstractmethod
  from typing_extensions import Self
  import numpy
  from alibi_detect.exceptions import NotFittedError, ThresholdNotInferredError
label_desc: Use image node_id 4 for calling the SklearnOutlierDetector obj's underlying member method code with example usage: obj._to_backend_dtype(x) and returns: np