code: def test_index(
      self,
      same_inputs,
      get,
      index_layer,
      mask_constant,
      idx,
      readout,
  ):
      if index_layer == 3 and isinstance(idx, tuple) and len(idx) > 2:
          raise absltest.SkipTest(
              f"Readout outputs have only 2 dimensions, but "
              f"the index has {len(idx)}."
          )
  
      if get == "cov2" and same_inputs:
          raise absltest.SkipTest("cov2 is None when x2 is None.")
  
      width = 2**7
      n_samples = 2**7
      tol = 0.05
      key1, key2, key_mc = random.split(random.PRNGKey(1), 3)
  
      x1 = jnp.cos(random.normal(key1, [6, 3, 4, 5]))
      if mask_constant is not None:
          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, [7, 3, 4, 5]))
          if mask_constant is not None:
              mask2 = random.bernoulli(key2, p=0.1, shape=x2.shape)
              x2 = jnp.where(mask2, mask_constant, x2)
  
      canonical_idx = utils.canonicalize_idx(
          idx=idx, ndim=x1.ndim if index_layer != 3 else 2
      )
  
      filter_shape = (2, 3)
      if index_layer == 0:
          for i, s in enumerate(canonical_idx):
              if isinstance(s, int) and i in (1, 2):
                  filter_shape = filter_shape[:-1]
  
      layers = [
          stax.Conv(width, filter_shape, padding="SAME"),
          stax.Relu(),
          readout(),
          stax.Dense(1 if get == "ntk" else width),
      ]
  
      layers.insert(index_layer, stax.Index(idx=idx))
      init_fn, apply_fn, kernel_fn = stax.serial(*layers)
  
      def get_exact():
          return kernel_fn(x1, x2, get, mask_constant=mask_constant)
  
      if isinstance(canonical_idx[0], int) or canonical_idx[
          -1
      ] != slice(None):
          # Unsupported integer indexing into batch axis, or any indexing into
          # the channel axis.
          self.assertRaises(NotImplementedError, get_exact)
  
      else:
          exact = get_exact()
  
          if get in ("cov1", "cov2"):
              diagonal_axes = (0,)
              get_e = "nngp"
              if get == "cov1":
                  x1_e, x2_e = x1, None
              elif get == "cov2":
                  x1_e, x2_e = x2, None
          else:
              diagonal_axes = ()
              x1_e, x2_e = x1, x2
              get_e = get
  
          kernel_fn_mc = nt.monte_carlo_kernel_fn(
              init_fn=init_fn,
              apply_fn=stax.unmask_fn(apply_fn),
              key=key_mc,
              n_samples=n_samples,
              device_count=0,
              diagonal_axes=diagonal_axes,
              implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          )
          empirical = kernel_fn_mc(
              x1_e, x2_e, get_e, mask_constant=mask_constant
          )
  
          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 1 for calling the IndexTest obj's underlying member method code with example usage: obj.test_index(same_inputs, get, index_layer, mask_constant, idx, readout) and returns: kernel_fn