class_docstr: Base class for torch backend outlier detection algorithms.
class_docstr_tok: ["Base","class","for","torch","backend","outlier","detection","algorithms","."]
code: def __init__(self, device: TorchDeviceType = None):
      self.device = get_device(device)
      super().__init__()
  
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 1 to create a new TorchOutlierDetector object from inherited base classes: torch.nn, FitMixinTorch, ABC with example: obj = TorchOutlierDetector(device)