code: def test_elementwise(
      self, same_inputs, phi, n, diagonal_batch, diagonal_spatial
  ):
      fn = lambda x: phi[1]((), x)
  
      name = phi[0].__name__
  
      def nngp_fn(cov12, var1, var2):
          if "Identity" in name:
              res = cov12
  
          elif "Erf" in name:
              prod = (1 + 2 * var1) * (1 + 2 * var2)
              res = jnp.arcsin(2 * cov12 / jnp.sqrt(prod)) * 2 / jnp.pi
  
          elif "Sin" in name:
              sum_ = var1 + var2
              s1 = jnp.exp((-0.5 * sum_ + cov12))
              s2 = jnp.exp((-0.5 * sum_ - cov12))
              res = (s1 - s2) / 2
  
          elif "Relu" in name:
              prod = var1 * var2
              sqrt = jnp.sqrt(jnp.maximum(prod - cov12**2, 1e-30))
              angles = jnp.arctan2(sqrt, cov12)
              dot_sigma = (1 - angles / jnp.pi) / 2
              res = sqrt / (2 * jnp.pi) + dot_sigma * cov12
  
          else:
              raise NotImplementedError(name)
  
          return res
  
      _, _, kernel_fn = stax.serial(
          stax.Dense(1),
          stax.Elementwise(fn, nngp_fn),
          stax.Dense(1),
          stax.Elementwise(fn, nngp_fn),
      )
      _, _, kernel_fn_manual = stax.serial(
          stax.Dense(1), phi, stax.Dense(1), phi
      )
  
      key = random.PRNGKey(1)
      shape = (4, 3, 2)[:n] + (1,)
      x1 = random.normal(key, (5,) + shape)
      if same_inputs is None:
          x2 = None
      elif same_inputs is True:
          x2 = x1
      else:
          x2 = random.normal(key, (6,) + shape)
  
      kwargs = dict(
          diagonal_batch=diagonal_batch,
          diagonal_spatial=diagonal_spatial,
      )
  
      k = kernel_fn(x1, x2, **kwargs)
      k_manual = kernel_fn_manual(x1, x2, **kwargs).replace(
          is_gaussian=False
      )
      self.assertAllClose(k_manual, k)
  
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 1 for calling the ElementwiseTest obj's underlying member method code with example usage: obj.test_elementwise(same_inputs, phi, n, diagonal_batch, diagonal_spatial) and returns: res