code: def test_flatten(self, same_inputs):
      key = random.PRNGKey(1)
      X0_1 = random.normal(key, (4, 4, 3, 2))
      X0_2 = None if same_inputs else random.normal(key, (2, 4, 3, 2))
  
      X0_1_flat = jnp.reshape(X0_1, (X0_1.shape[0], -1))
      X0_2_flat = (
          None
          if X0_2 is None
          else jnp.reshape(X0_2, (X0_2.shape[0], -1))
      )
  
      dense = stax.Dense(512, 1.7, 0.1)
      init_fc, apply_fc, kernel_fc = stax.serial(
          dense, stax.Erf(), dense
      )
      init_top, apply_top, kernel_top = stax.serial(
          dense, stax.Erf(), dense, stax.Flatten()
      )
      init_mid, apply_mid, kernel_mid = stax.serial(
          dense, stax.Erf(), stax.Flatten(), dense
      )
      init_bot, apply_bot, kernel_bot = stax.serial(
          stax.Flatten(), dense, stax.Erf(), dense
      )
  
      kernel_fc = jit(kernel_fc)
      kernel_top = jit(kernel_top)
      kernel_mid = jit(kernel_mid)
      kernel_bot = jit(kernel_bot)
  
      n = 100
  
      kernel_fc_mc = nt.monte_carlo_kernel_fn(
          init_fc,
          apply_fc,
          key,
          n,
          vmap_axes=0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )
      kernel_bot_mc = nt.monte_carlo_kernel_fn(
          init_bot,
          apply_bot,
          key,
          n,
          vmap_axes=0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )
      kernel_mid_mc = nt.monte_carlo_kernel_fn(
          init_mid,
          apply_mid,
          key,
          n,
          vmap_axes=0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )
      kernel_top_mc = nt.monte_carlo_kernel_fn(
          init_top,
          apply_top,
          key,
          n,
          vmap_axes=0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )
  
      K = kernel_fc(X0_1_flat, X0_2_flat)
  
      K_bot = kernel_bot(X0_1, X0_2)
      K_bot_flat = kernel_bot(X0_1_flat, X0_2_flat)
      self.assertAllClose(K_bot, K)
      self.assertAllClose(K_bot_flat, K)
  
      def assert_close(a, b):
          self.assertAllClose(a, b, atol=0.05, rtol=0.02)
  
      K_fc_mc = kernel_fc_mc(X0_1_flat, X0_2_flat, get="nngp")
      K_bot_mc = kernel_bot_mc(X0_1, X0_2, get="nngp")
      K_bot_flat_mc = kernel_bot_mc(X0_1_flat, X0_2_flat, get="nngp")
  
      assert_close(K_fc_mc, K.nngp)
      assert_close(K_bot_mc, K_bot.nngp)
      assert_close(K_bot_flat_mc, K_bot_flat.nngp)
  
      K_mid = kernel_mid(X0_1, X0_2)
      K_mid_flat = kernel_mid(X0_1_flat, X0_2_flat)
  
      K_mid_mc = kernel_mid_mc(X0_1, X0_2, get="nngp")
      K_mid_flat_mc = kernel_mid_mc(X0_1_flat, X0_2_flat, get="nngp")
  
      assert_close(K_mid_mc, K_mid.nngp)
      assert_close(K_mid_flat, K)
      assert_close(K_mid_flat_mc, K_mid_flat.nngp)
  
      K_top = kernel_top(X0_1, X0_2).replace(
          is_gaussian=True, shape1=K_mid.shape1, shape2=K_mid.shape2
      )
      K_top_flat = kernel_top(X0_1_flat, X0_2_flat).replace(
          is_gaussian=True
      )
  
      K_top_mc = kernel_top_mc(X0_1, X0_2, get="nngp")
      K_top_flat_mc = kernel_top_mc(X0_1_flat, X0_2_flat, get="nngp")
  
      assert_close(K_top_flat, K)
      assert_close(K_top_mc, K_top.nngp)
      assert_close(K_top_flat_mc, K_top_flat.nngp)
  
      assert_close(K_top, K_mid)
  
imports: import itertools
  import random
  import string
  import time
  from absl.testing import absltest
  from jax import default_backend
  from jax import jit
  from jax import lax
  from jax import random
  from jax import vjp
  from jax import config
  import jax.numpy
  import more_itertools
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
  from neural_tangents._src.utils import utils
  from tests import test_utils
  import numpy
label_desc: Use image node_id 1 for calling the FlattenTest obj's underlying member method code with example usage: obj.test_flatten(same_inputs) without return types