code: def _test_activation(
      self, activation_fn, same_inputs, model, get, rbf_gamma=None
  ):
      if "conv" in model:
          test_utils.skip_test(self)
  
      key = random.PRNGKey(1)
      key, split = random.split(key)
      output_dim = 1024 if get == "nngp" else 1
      b_std = 0.5
      W_std = 2.0
      if activation_fn[2].__name__ == "Sin":
          W_std = 0.9
      if activation_fn[2].__name__ == "Rbf":
          W_std = 1.0
          b_std = 0.0
  
      if model == "fc":
          rtol = 0.04
          X0_1 = random.normal(key, (4, 2))
          X0_2 = None if same_inputs else random.normal(split, (2, 2))
          affine = stax.Dense(1024, W_std, b_std)
          readout = stax.Dense(output_dim)
          depth = 1
  
      else:
          rtol = 0.05
          X0_1 = random.normal(key, (2, 4, 4, 3))
          X0_2 = (
              None
              if same_inputs
              else random.normal(split, (4, 4, 4, 3))
          )
          affine = stax.Conv(
              512, (3, 2), W_std=W_std, b_std=b_std, padding="SAME"
          )
          readout = stax.serial(
              stax.GlobalAvgPool()
              if "pool" in model
              else stax.Flatten(),
              stax.Dense(output_dim),
          )
          depth = 2
  
      if default_backend() == "cpu":
          num_samplings = 200
          rtol *= 2
      else:
          num_samplings = (
              500
              if activation_fn[2].__name__ in ("Sin", "Rbf")
              else 300
          )
  
      init_fn, apply_fn, kernel_fn = stax.serial(
          *[affine, activation_fn] * depth, readout
      )
      analytic_kernel = kernel_fn(X0_1, X0_2, get)
      mc_kernel_fn = nt.monte_carlo_kernel_fn(
          init_fn,
          apply_fn,
          split,
          num_samplings,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=0,
      )
      empirical_kernel = mc_kernel_fn(X0_1, X0_2, get)
      test_utils.assert_close_matrices(
          self, analytic_kernel, empirical_kernel, rtol
      )
  
      # Check match with explicit RBF
      if rbf_gamma is not None and get == "nngp" and model == "fc":
          input_dim = X0_1.shape[1]
          _, _, kernel_fn = self._RBF(rbf_gamma / input_dim)
          direct_rbf_kernel = kernel_fn(X0_1, X0_2, get)
          test_utils.assert_close_matrices(
              self, analytic_kernel, direct_rbf_kernel, rtol
          )
  
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 6 for calling the ActivationTest obj's underlying member method code with example usage: obj._test_activation(activation_fn, same_inputs, model, get, rbf_gamma) without return types