code: def testValuesAreCorrectScalarTransform(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
  
      bij = scale.Scale(scale=self.dtype(2.0))
  
      # Flat multiplication by 2.
      def scale_transform(x, feature_ndims, param_expansion_ndims):
          del feature_ndims, param_expansion_ndims
          return bij.forward(x)
  
      scale_transformed_kernel = feature_transformed.FeatureTransformed(
          kernel, transformation_fn=scale_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,
              2.0 * x,
              2.0 * y,
              feature_ndims=feature_ndims,
          ),
          self.evaluate(scale_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 2 for calling the _FeatureTransformedTest obj's underlying member method code with example usage: obj.testValuesAreCorrectScalarTransform(feature_ndims, dims) and returns: bij