code: def test_stddrift(stddrift_params):
      backend = stddrift_params
      if backend.lower() == "pytorch":
          kernel = MyKernelTorch(n_features)
      elif backend.lower() == "tensorflow":
          kernel = MyKernelTF((n_features,))
      else:
          kernel = None
      x_ref = np.random.randn(*(n, n_features))
  
      try:
          cd = SpotTheDiffDrift(
              x_ref=x_ref, kernel=kernel, backend=backend
          )
      except NotImplementedError:
          cd = None
  
      if backend.lower() == "pytorch":
          assert isinstance(cd._detector, SpotTheDiffDriftTorch)
      elif backend.lower() == "tensorflow":
          assert isinstance(cd._detector, SpotTheDiffDriftTF)
      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 SpotTheDiffDrift
  from alibi_detect.cd.pytorch.spot_the_diff import SpotTheDiffDriftTorch
  from alibi_detect.cd.tensorflow.spot_the_diff import SpotTheDiffDriftTF
label_desc: Use image node_id 2 for calling a global function with example usage: test_stddrift(stddrift_params) without return types