code: def _RBF(self, gamma):
      init_fn = lambda key, input_shape: (input_shape, ())
  
      def apply_fn(unused_params, unused_xs, **kwargs):
          raise NotImplementedError()
  
      def kernel_fn(kernels, **kwargs):
          if kernels.ntk is not None:
              raise ValueError(
                  "RBF Kernel does not have an associated NTK."
              )
  
          if kernels.nngp.ndim > 2:
              raise ValueError(
                  (
                      "RBF Kernel is not defined for covariance matrices with dimension"
                      " greater than two."
                  )
              )
  
          input_dim = kernels.shape1[1]
          cov1 = kernels.cov1
          cov1 = jnp.reshape(cov1, (cov1.shape[0], 1))
          cov2 = cov1 if kernels.cov2 is None else kernels.cov2
          cov2 = jnp.reshape(cov2, (1, cov2.shape[0]))
          nngp = kernels.nngp
  
          # TODO(schsam): Update cov1 and cov2 if we want to compose this kernel
          # with other kernels.
          return kernels.replace(
              nngp=jnp.exp(
                  -input_dim * gamma * (cov1 + cov2 - 2 * nngp)
              )
          )
  
      return init_fn, apply_fn, kernel_fn
  
imports: import itertools
  import random
  from absl.testing import absltest
  from jax import config
  from jax import default_backend
  from jax import grad, jacfwd, jacrev, jit, jvp, value_and_grad
  from jax import random
  import jax.numpy
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
  from tests import test_utils
label_desc: Use image node_id 5 for calling the ActivationTest obj's underlying member method code with example usage: obj._RBF(gamma) and returns: init_fn,  apply_fn,  kernel_fn, kernels