code: def test_parallel_in_out(self, same_inputs, kernel_type):
      platform = default_backend()
      rtol = RTOL if platform != "tpu" else 0.05
  
      rng = random.PRNGKey(0)
      input_key1, input_key2, mc_key = random.split(rng, 3)
  
      x1_1, x2_1 = _get_inputs(input_key1, same_inputs, (BATCH_SIZE, 1))
      x1_2, x2_2 = _get_inputs(input_key2, same_inputs, (BATCH_SIZE, 2))
  
      x1 = (x1_1, x1_2)
      x2 = (x2_1, x2_2)
  
      N_in = 2**10
      N_out = N_in if kernel_type == "nngp" else 1
  
      readin = stax.serial(
          stax.parallel(stax.Dense(N_in), stax.Dense(N_in)),
          stax.FanInSum(),
      )
      readout = stax.serial(
          stax.FanOut(3),
          stax.parallel(
              stax.Dense(N_out),
              stax.Dense(N_out + 1),
              stax.Dense(N_out + 2),
          ),
      )
      init_fn, apply_fn, _ = stax.serial(readin, readout)
  
      K_readin_fn = jit(readin[2])
      K_readout_fn = jit(functools.partial(readout[2], get=kernel_type))
  
      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, 0, 0], {}),
      )
  
      test_utils.assert_close_matrices(
          self,
          K_readout_fn(K_readin_fn(x1, x2)),
          kernel_fn_empirical(x1, x2, get=kernel_type),
          rtol,
      )
  
      # Check Both (here we just want to make sure we _can_ compute the output).
      K_readin_fn = jit(readin[2])
      K_readout_fn = jit(
          functools.partial(readout[2], get=("nngp", "ntk"))
      )
  
      K_readout_fn(K_readin_fn(x1, x2))
  
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 3 for calling the ParallelInOutTest obj's underlying member method code with example usage: obj.test_parallel_in_out(same_inputs, kernel_type) without return types