code: def testValuesAreCorrectVectorTransform(self, feature_ndims, dims):
      amplitude = self.dtype(5.0)
      length_scale = self.dtype(0.2)
      kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          amplitude, length_scale, feature_ndims=feature_ndims
      )
      input_shape = [dims] * feature_ndims
  
      scale_diag = np.random.uniform(-1, 1, size=(dims,)).astype(
          self.dtype
      )
      bij = scale_matvec_diag.ScaleMatvecDiag(scale_diag=scale_diag)
  
      # Scaling the last dimension.
      def vector_transform(x, feature_ndims, param_expansion_ndims):
          del feature_ndims, param_expansion_ndims
          return bij.forward(x)
  
      vector_transformed_kernel = (
          feature_transformed.FeatureTransformed(
              kernel, transformation_fn=vector_transform
          )
      )
  
      x = np.random.uniform(-1, 1, size=input_shape).astype(self.dtype)
      y = np.random.uniform(-1, 1, size=input_shape).astype(self.dtype)
      self.assertAllClose(
          _numpy_exp_quad(
              amplitude,
              length_scale,
              scale_diag * x,
              scale_diag * y,
              feature_ndims=feature_ndims,
          ),
          self.evaluate(vector_transformed_kernel.apply(x, y)),
      )
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/feature_transformed_test.py
imports: from absl.testing import parameterized
  import numpy
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_diag
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels import feature_transformed
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 3 for calling the _FeatureTransformedTest obj's underlying member method code with example usage: obj.testValuesAreCorrectVectorTransform(feature_ndims, dims) and returns: bij