code: def test_activation(
      self, same_inputs, model, phi_name, get, abc, approximate
  ):
      if abc != [0.3, 1.5, -jnp.pi / 4]:
          test_utils.skip_test(self)
  
      if approximate and phi_name != "Gelu":
          raise absltest.SkipTest(
              f"{phi_name} does not have an `approximate parameter."
          )
  
      a, b, c = abc
      if phi_name == "Sin":
          activation = stax.Sin(a=a, b=b, c=c)
      elif phi_name == "Erf":
          activation = stax.Erf(a=a, b=b, c=c)
      elif phi_name in ["Gelu", "Sign", "Cos"]:
          if a != 0.3 or b != 0.3 or c != 0.0:
              raise absltest.SkipTest(
                  "Skip `Gelu/Sign/Cos` test if "
                  " (a, b, c) != (.3, .3, 0.)."
              )
          activation = (
              stax.Gelu() if phi_name == "Gelu" else stax.Sign()
          )
      else:
          raise NotImplementedError(
              f"Activation {phi_name} is not implemented."
          )
      self._test_activation(activation, same_inputs, model, get)
  
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 7 for calling the ActivationTest obj's underlying member method code with example usage: obj.test_activation(same_inputs, model, phi_name, get, abc, approximate) without return types