code: def test_parallel_in_out_mc(self, same_inputs, batch_size):
      rng = random.PRNGKey(0)
      input_key1, input_key2, net_key = random.split(rng, 3)
  
      x1_1, x1_2, x1_3 = random.normal(input_key1, (3, 2, 5))
      x1 = (x1_1, (x1_2, x1_3))
  
      if same_inputs:
          x2 = None
      else:
          x2_1, x2_2, x2_3 = random.normal(input_key2, (3, 4, 5))
          x2 = (x2_1, (x2_2, x2_3))
  
      def net(N_out):
          return stax.parallel(
              stax.Dense(N_out),
              stax.parallel(
                  stax.Dense(N_out + 1), stax.Dense(N_out + 2)
              ),
          )
  
      # Check NNGP.
      init_fn, apply_fn, _ = net(WIDTH)
  
      nb_kernel_fn = monte_carlo.monte_carlo_kernel_fn(
          init_fn, apply_fn, net_key, n_samples=4, trace_axes=(-1,)
      )
  
      kernel_fn = monte_carlo.monte_carlo_kernel_fn(
          init_fn,
          apply_fn,
          net_key,
          n_samples=4,
          batch_size=batch_size,
          trace_axes=(-1,),
      )
  
      self.assertAllClose(
          kernel_fn(x1, x2, "nngp"), nb_kernel_fn(x1, x2, "nngp")
      )
  
file_path: neural-tangents/tests/monte_carlo_test.py
imports: from absl.testing import absltest
  import jax
  from jax import config
  from jax import random
  import jax.numpy
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src import batching
  from neural_tangents._src import monte_carlo
  from tests import test_utils
label_desc: Use image node_id 6 for calling the MonteCarloTest obj's underlying member method code with example usage: obj.test_parallel_in_out_mc(same_inputs, batch_size) and returns: stax