code: def _compute_additive_kernel(
      self, amplitudes, length_scale, dim, x, y, method="apply"
  ):
      expected = 0.0
      for i in range(amplitudes.shape[-1]):
          # Get all (ordered) combinations of indices of length `i + 1`
          ind = itertools.combinations(range(dim), i + 1)
  
          # Sum over all combinations of indices of the given length.
          sum_i = 0.0
          for ind_i in ind:
              # Multiply the kernel values at the given indices together.
              prod_k = 1.0
              for d in ind_i:
                  kernel = (
                      exponentiated_quadratic.ExponentiatedQuadratic(
                          amplitude=1.0,
                          length_scale=length_scale[..., d],
                      )
                  )
                  if method == "apply":
                      prod_k *= kernel.apply(
                          x[..., d, np.newaxis], y[..., d, np.newaxis]
                      )
                  elif method == "matrix":
                      prod_k *= kernel.matrix(
                          x[..., d, np.newaxis], y[..., d, np.newaxis]
                      )
              sum_i += prod_k
          expected += amplitudes[..., i] ** 2 * sum_i
      return expected
  
file_path: probability/tensorflow_probability/python/experimental/psd_kernels/additive_kernel_test.py
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.psd_kernels import additive_kernel
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
label_desc: Use image node_id 2 for calling the AdditiveKernelTest obj's underlying member method code with example usage: obj._compute_additive_kernel(amplitudes, length_scale, dim, x, y, method) and returns: expected