code: def test_ab_relu_id(self, same_inputs, do_stabilize):
      key = random.PRNGKey(1)
      X0_1 = random.normal(key, (3, 2))
      fc = stax.Dense(5, 1, 0)
  
      X0_2 = None if same_inputs else random.normal(key, (4, 2))
  
      # Test that ABRelu(a, a) == a * Identity
      init_fn, apply_id, kernel_fn_id = stax.serial(fc, stax.Identity())
      _, params = init_fn(key, input_shape=X0_1.shape)
  
      for a in [-5, -1, -0.5, 0, 0.5, 1, 5]:
          with self.subTest(a=a):
              _, apply_ab_relu, kernel_fn_ab_relu = stax.serial(
                  fc, stax.ABRelu(a, a, do_stabilize=do_stabilize)
              )
  
              X1_1_id = a * apply_id(params, X0_1)
              X1_1_ab_relu = apply_ab_relu(params, X0_1)
              self.assertAllClose(X1_1_id, X1_1_ab_relu)
  
              kernels_id = kernel_fn_id(
                  X0_1 * a, None if X0_2 is None else a * X0_2
              )
              kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2)
              # Manually correct the value of `is_gaussian` because
              # `ab_relu` (incorrectly) sets `is_gaussian=False` when `a==b`.
              kernels_ab_relu = kernels_ab_relu.replace(
                  is_gaussian=True
              )
              self.assertAllClose(kernels_id, kernels_ab_relu)
  
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 2 for calling the ABReluTest obj's underlying member method code with example usage: obj.test_ab_relu_id(same_inputs, do_stabilize) without return types