code: def test_lof_kernel(ensembler):
      """
      Test the lof torch backend can be correctly initialized with a kernel, fit
      on data and used to predict outliers.
      """
  
      kernel = GaussianRBF(sigma=torch.tensor((1)))
      lof_torch = LOFTorch(k=[4, 5], kernel=kernel, ensembler=ensembler)
      x_ref = torch.randn((1024, 10))
      lof_torch.fit(x_ref)
      x = torch.randn((3, 10)) * torch.tensor([[1], [1], [100]])
      lof_torch.infer_threshold(x_ref, 0.1)
      outputs = lof_torch.predict(x)
      assert torch.all(outputs.is_outlier == torch.tensor([0, 0, 1]))
      assert torch.all(lof_torch(x) == torch.tensor([0, 0, 1]))
  
file_path: alibi-detect/alibi_detect/od/tests/test__lof/test__lof_backend.py
imports: import pytest
  import torch
  from alibi_detect.od.pytorch.lof import LOFTorch
  from alibi_detect.utils.pytorch.kernels import GaussianRBF
  from alibi_detect.od.pytorch.ensemble import Ensembler, PValNormalizer, AverageAggregator
  from alibi_detect.exceptions import NotFittedError, ThresholdNotInferredError
label_desc: Use image node_id 6 for calling a global function with example usage: test_lof_kernel(ensembler) without return types