class_docstr: Base class for torch backend outlier detection algorithms.
class_docstr_tok: ["Base","class","for","torch","backend","outlier","detection","algorithms","."]
docstr: Classify the data as outlier or not.
  
  Parameters
  ----------
  scores
      Scores to classify. Larger scores indicate more likely outliers.
  
  Returns
  -------
  `torch.Tensor` or ``None``
docstr_tok: ["Classify","the","data","as","outlier","or","not",".","Parameters","--","--","--","--","--","scores","Scores","to","classify",".","Larger","scores","indicate","more","likely","outliers",".","Returns","--","--","--","-","`","torch.Tensor","`","or","``","None","``"]
code: def _classify_outlier(self, scores: torch.Tensor) -> torch.Tensor:
      """Classify the data as outlier or not.
  
      Parameters
      ----------
      scores
          Scores to classify. Larger scores indicate more likely outliers.
  
      Returns
      -------
      `torch.Tensor` or ``None``
      """
      return (
          (scores > self.threshold).to(torch.int8)
          if self.threshold_inferred
          else None
      )
  
imports: from typing import List, Union, Optional, Dict
  from dataclasses import dataclass, fields
  from abc import ABC, abstractmethod
  import numpy
  import torch
  from alibi_detect.od.pytorch.ensemble import FitMixinTorch
  from alibi_detect.utils.pytorch.misc import get_device
  from alibi_detect.exceptions import ThresholdNotInferredError
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 7 for calling the TorchOutlierDetector obj's underlying member method code with example usage: obj._classify_outlier(scores) and returns: unknown