code: def test_parallel_out(self, same_inputs, kernel_type):
      platform = default_backend()
      rtol = RTOL if platform != "tpu" else 0.05
  
      rng = random.PRNGKey(0)
      input_key1, mc_key = random.split(rng, 2)
  
      x1, x2 = _get_inputs(input_key1, same_inputs, (BATCH_SIZE, 1))
  
      N = 2**10
  
      def net(logits):
          return stax.serial(
              stax.Dense(N),
              stax.FanOut(2),
              stax.parallel(stax.Dense(logits), stax.Dense(logits)),
          )
  
      init_fn, apply_fn, kernel_fn = net(
          N if kernel_type == "nngp" else 1
      )
  
      kernel_fn_empirical = nt.monte_carlo_kernel_fn(
          init_fn,
          apply_fn,
          mc_key,
          N_SAMPLES,
          trace_axes=(-1,),
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=(0, [0, 0], {}),
      )
  
      test_utils.assert_close_matrices(
          self,
          kernel_fn(x1, x2, kernel_type),
          kernel_fn_empirical(x1, x2, kernel_type),
          rtol,
      )
  
imports: import functools
  import itertools
  import random
  from absl.testing import absltest
  from jax import config
  from jax import default_backend
  from jax import jit
  from jax import random
  from jax.example_libraries import stax
  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
  import numpy
label_desc: Use image node_id 2 for calling the ParallelInOutTest obj's underlying member method code with example usage: obj.test_parallel_out(same_inputs, kernel_type) and returns: stax