docstr: Compute the outlier score.
  
  Centers the data and projects it onto the principal components. The score is then the sum of the
  squared projections.
  
  Parameters
  ----------
  x
      The test data.
  
  Returns
  -------
  The outlier score.
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code: def _score(self, x: torch.Tensor) -> torch.Tensor:
      """Compute the outlier score.
  
      Centers the data and projects it onto the principal components. The score is then the sum of the
      squared projections.
  
      Parameters
      ----------
      x
          The test data.
  
      Returns
      -------
      The outlier score.
      """
      x_cen = x - self.x_ref_mean
      x_pcs = x_cen @ self.pcs
      return (x_pcs**2).sum(1)
  
imports: from typing import Optional, Callable
  import torch
  from alibi_detect.od.pytorch.base import TorchOutlierDetector
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 3 for calling the LinearPCATorch obj's underlying member method code with example usage: obj._score(x) and returns: unknown