code: def test_kernel_transform_num_features_mismatch(make_data):
      data, _ = make_data()
      t = ClinicalKernelTransform()
      t.fit(data)
  
      array_test = np.zeros((2, 17), dtype=float)
  
      error_msg = r"X has 17 features, but ClinicalKernelTransform is expecting 4 features as input\."
      warn_msg = "X does not have valid feature names, but ClinicalKernelTransform was fitted with feature names"
      with pytest.raises(ValueError, match=error_msg), pytest.warns(
          UserWarning, match=warn_msg
      ):
          t.transform(array_test)
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  import pytest
  from sklearn.base import clone
  from sklearn.metrics.pairwise import pairwise_kernels
  from sksurv.kernels import ClinicalKernelTransform, clinical_kernel
label_desc: Use image node_id 10 for calling the TestClinicalKernel obj's underlying member method code with example usage: obj.test_kernel_transform_num_features_mismatch(make_data) without return types