code: def test_image_resize(
      self,
      same_inputs,
      n,
      channel_axis,
      diagonal_spatial,
      diagonal_batch,
      batch_axis,
      method,
      antialias,
      precision,
      shape,
  ):
      if n > 2:
          test_utils.skip_test(self)
  
      n_b1, n_b2 = 2, 4
      n_c = 1
      key1, key2, _ = random.split(random.PRNGKey(1), 3)
  
      shape = shape[:channel_axis] + (-1,) + shape[channel_axis + 1 :]
  
      x_shape_n_c = [2, 4, 6, 8, 10, 12, 14][: n - 2]
      x_shape = list(x_shape_n_c)
  
      for a in sorted((batch_axis, channel_axis)):
          x_shape.insert(a, n_b1 if a == batch_axis else n_c)
  
      mask_constant = 10.0
  
      x1 = jnp.cos(random.normal(key1, x_shape))
      mask1 = random.bernoulli(key1, p=0.3, shape=x1.shape)
      x1 = jnp.where(mask1, mask_constant, x1)
  
      if same_inputs:
          x2 = None
      else:
          x2_shape = (
              x_shape[:batch_axis] + [n_b2] + x_shape[batch_axis + 1 :]
          )
          x2 = jnp.cos(random.normal(key2, x2_shape))
          mask2 = random.bernoulli(key2, p=0.2, shape=x2.shape)
          x2 = jnp.where(mask2, mask_constant, x2)
  
      init_fn, apply_fn, kernel_fn = stax.ImageResize(
          method=method,
          antialias=antialias,
          precision=precision,
          batch_axis=batch_axis,
          channel_axis=channel_axis,
          shape=shape,
      )
  
      def get_exact():
          return kernel_fn(
              x1,
              x2,
              diagonal_spatial=diagonal_spatial,
              diagonal_batch=diagonal_batch,
              batch_axis=batch_axis,
              channel_axis=channel_axis,
              mask_constant=mask_constant,
          )
  
      if (shape[batch_axis] != -1 and diagonal_batch) or (
          any(
              shape[i] != -1
              for i in range(len(shape))
              if i not in (batch_axis, channel_axis)
          )
          and diagonal_spatial
      ):
          self.assertRaises(ValueError, get_exact)
  
      else:
          exact = get_exact()
  
          def get_empirical(get):
              def get_diagonal_axes():
                  axes = ()
                  if get in ("cov1", "cov2") and diagonal_batch:
                      axes += (batch_axis,)
  
                  if diagonal_spatial:
                      axes += tuple(
                          i
                          for i in range(n)
                          if i not in (batch_axis, channel_axis)
                      )
  
                  return axes
  
              kernel_fn_mc = nt.monte_carlo_kernel_fn(
                  init_fn=init_fn,
                  apply_fn=stax.unmask_fn(apply_fn),
                  key=key1,
                  n_samples=1,
                  trace_axes=(channel_axis,),
                  diagonal_axes=get_diagonal_axes(),
                  device_count=-1
                  if (
                      get == "nngp"
                      and batch_axis == 0
                      and shape[batch_axis] == -1
                  )
                  else 0,
                  implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
              )
  
              empirical = kernel_fn_mc(
                  x1=x2 if get == "cov2" else x1,
                  x2=x2 if get == "nngp" else None,
                  get="nngp",
                  batch_axis=batch_axis,
                  channel_axis=channel_axis,
                  mask_constant=mask_constant,
              )
  
              def batch_axes():
                  axis = batch_axis
                  if channel_axis < batch_axis:
                      axis -= 1
                  if not diagonal_spatial:
                      axis *= 2
  
                  if get in ("cov1", "cov2") and diagonal_batch:
                      return (axis,), (0,)
                  return (axis, axis + 1), (0, 1)
  
              empirical = jnp.moveaxis(empirical, *batch_axes())
              return empirical
  
          for get in ("nngp", "cov1", "cov2"):
              if get == "cov2" and same_inputs:
                  continue
  
              with self.subTest(get=get):
                  tol = 1e-2 if default_backend() == "tpu" else 1e-5
                  test_utils.assert_close_matrices(
                      self, get_empirical(get), getattr(exact, get), 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 1 for calling the ImageResizeTest obj's underlying member method code with example usage: obj.test_image_resize(same_inputs, n, channel_axis, diagonal_spatial, diagonal_batch, batch_axis, method, antialias, precision, shape) and returns: kernel_fn, empirical, axes, axis,  unknown,  int,  int, axis,  int