code: def test_image_resize_nn(
      self, same_inputs, get, n, do_pool, bottom_layer, method, shape
  ):
      if n != 2:
          test_utils.skip_test(self)
  
      width = 2**7
      n_samples = 2**7
      tol = 0.03
      key1, key2, _ = random.split(random.PRNGKey(1), 3)
  
      mask_constant = 10.0
  
      x_shape = [6, 3, 4, 5][: n - 1] + [1]
      x1 = jnp.cos(random.normal(key1, x_shape))
      mask1 = random.bernoulli(key1, p=0.2, shape=x1.shape)
      x1 = jnp.where(mask1, mask_constant, x1)
  
      if same_inputs:
          x2 = None
      else:
          x2 = jnp.cos(random.normal(key2, x_shape))
          mask2 = random.bernoulli(key2, p=0.1, shape=x2.shape)
          x2 = jnp.where(mask2, mask_constant, x2)
  
      bottom = {
          "conv": stax.Conv(width, (3,) * (n - 2), padding="SAME"),
          "relu": stax.serial(
              stax.Conv(width, (3,) * (n - 2), padding="SAME"),
              stax.Relu(),
          ),
          "resize": stax.Identity(),
      }[bottom_layer]
  
      init_fn, apply_fn, kernel_fn = stax.serial(
          bottom,
          stax.ImageResize(method=method, shape=shape),
          stax.Conv(width, (2,), padding="SAME"),
          stax.Relu(),
          (stax.GlobalAvgPool() if do_pool else stax.Flatten()),
          stax.Dense(width if get == "nngp" else 1, 0.9, 0.1),
      )
  
      kernel_fn_mc = nt.monte_carlo_kernel_fn(
          init_fn=init_fn,
          apply_fn=stax.unmask_fn(apply_fn),
          key=key1,
          n_samples=n_samples,
          device_count=0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )
  
      empirical = kernel_fn_mc(x1, x2, get, mask_constant=mask_constant)
  
      def get_exact():
          return kernel_fn(x1, x2, get, mask_constant=mask_constant)
  
      if shape[-1] != -1:
          # Make sure an error is thrown if resizing a channel axis is requested.
          self.assertRaises(ValueError, get_exact)
      else:
          exact = get_exact()
          test_utils.assert_close_matrices(self, empirical, exact, tol)
  
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 2 for calling the ImageResizeTest obj's underlying member method code with example usage: obj.test_image_resize_nn(same_inputs, get, n, do_pool, bottom_layer, method, shape) and returns: kernel_fn