code: def test_lkdrift(lkdrift_params):
      backend = lkdrift_params
      if backend.lower() == "pytorch":
          kernel = MyKernelTorch(n_features)
      elif backend.lower() == "tensorflow":
          kernel = MyKernelTF(n_features)
      elif has_keops and backend.lower() == "keops":
          kernel = MyKernelKeops()
      else:
          kernel = None
      x_ref = np.random.randn(*(n, n_features))
  
      try:
          cd = LearnedKernelDrift(
              x_ref=x_ref, kernel=kernel, backend=backend
          )
      except NotImplementedError:
          cd = None
      except ImportError:
          assert not has_keops
          cd = None
  
      if backend.lower() == "pytorch":
          assert isinstance(cd._detector, LearnedKernelDriftTorch)
      elif backend.lower() == "tensorflow":
          assert isinstance(cd._detector, LearnedKernelDriftTF)
      elif has_keops and backend.lower() == "keops":
          assert isinstance(cd._detector, LearnedKernelDriftKeops)
      else:
          assert cd is None
  
imports: import numpy
  import pytest
  import tensorflow
  from tensorflow.keras.layers import Dense
  import torch
  import torch.nn
  from alibi_detect.cd import LearnedKernelDrift
  from alibi_detect.cd.pytorch.learned_kernel import LearnedKernelDriftTorch
  from alibi_detect.cd.tensorflow.learned_kernel import LearnedKernelDriftTF
  from alibi_detect.utils.frameworks import has_keops
label_desc: Use image node_id 2 for calling a global function with example usage: test_lkdrift(lkdrift_params) without return types